-
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Tupelo, US-MS
Salary / Rate: Not Specified
Posted: 2026-07-15 10:38:31
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Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Jackson, US-MS
Salary / Rate: Not Specified
Posted: 2026-07-15 10:38:30
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Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Grand Rapids, US-MI
Salary / Rate: Not Specified
Posted: 2026-07-15 10:38:30
-
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Detroit, US-MI
Salary / Rate: Not Specified
Posted: 2026-07-15 10:38:26
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Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Marquette, US-MI
Salary / Rate: Not Specified
Posted: 2026-07-15 10:38:24
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Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Springfield, US-MA
Salary / Rate: Not Specified
Posted: 2026-07-15 10:38:23
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Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Duluth, US-MN
Salary / Rate: Not Specified
Posted: 2026-07-15 10:38:22
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Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Minneapolis, US-MN
Salary / Rate: Not Specified
Posted: 2026-07-15 10:38:19
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Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Baton Rouge, US-LA
Salary / Rate: Not Specified
Posted: 2026-07-15 10:38:17
-
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Baltimore, US-MD
Salary / Rate: Not Specified
Posted: 2026-07-15 10:38:14
-
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Portland, US-ME
Salary / Rate: Not Specified
Posted: 2026-07-15 10:38:11
-
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Aberdeen, US-MD
Salary / Rate: Not Specified
Posted: 2026-07-15 10:38:08
-
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Boston, US-MA
Salary / Rate: Not Specified
Posted: 2026-07-15 10:38:06
-
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Bangor, US-ME
Salary / Rate: Not Specified
Posted: 2026-07-15 10:38:03
-
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Bowling Green, US-KY
Salary / Rate: Not Specified
Posted: 2026-07-15 10:38:00
-
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: New Orleans, US-LA
Salary / Rate: Not Specified
Posted: 2026-07-15 10:37:59
-
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Shreveport, US-LA
Salary / Rate: Not Specified
Posted: 2026-07-15 10:37:58
-
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Louisville, US-KY
Salary / Rate: Not Specified
Posted: 2026-07-15 10:37:58
-
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Kansas City, US-KS
Salary / Rate: Not Specified
Posted: 2026-07-15 10:37:57
-
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Wichita, US-KS
Salary / Rate: Not Specified
Posted: 2026-07-15 10:37:54
-
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Indianapolis, US-IN
Salary / Rate: Not Specified
Posted: 2026-07-15 10:37:52
-
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Fort Wayne, US-IN
Salary / Rate: Not Specified
Posted: 2026-07-15 10:37:49
-
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Des Moines, US-IA
Salary / Rate: Not Specified
Posted: 2026-07-15 10:37:48
-
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Sioux City, US-IA
Salary / Rate: Not Specified
Posted: 2026-07-15 10:37:48
-
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK's Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.
Essential Skills - What You'll Bring
• Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations.
• Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS.
• Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming ser...
....Read more...
Type: Permanent Location: Davenport, US-IA
Salary / Rate: Not Specified
Posted: 2026-07-15 10:37:45
