-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Milwaukee, US-WI
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:17
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Morgantown, US-WV
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:17
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Burlington, US-VT
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:16
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Tysons, US-VA
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:15
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Richmond, US-VA
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:15
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Bennington, US-VT
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:14
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Salt Lake City, US-UT
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:13
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: St. George, US-UT
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:13
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Lubbock, US-TX
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:12
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: San Antonio, US-TX
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:11
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Houston, US-TX
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:11
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Dallas, US-TX
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:10
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Sioux Falls, US-SD
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:09
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Columbia, US-SC
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:09
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Nashville, US-TN
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:08
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Rapid City, US-SD
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:08
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Memphis, US-TN
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:07
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Charleston, US-SC
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:06
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Providence, US-RI
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:06
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Philadelphia, US-PA
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:05
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Pittsburgh, US-PA
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:05
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Newport, US-RI
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:04
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Portland, US-OR
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:03
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Columbus, US-OH
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:03
-
1.
Work with business stakeholders, BI developers, analysts, data engineers and the data architect to translate reporting and decision-making needs into clear, reusable analytics requirements and data product specifications.
2.
Design, build and maintain analytics-ready data models, including curated datasets, fact and dimension models, reusable metric logic and semantic structures that support consistent self-service analytics.
3.
Develop modular transformation logic using SQL and modern analytics engineering approaches, ensuring business rules are transparent, version controlled, reusable and easy to maintain as organisational needs change.
4.
Partner with data engineers to shape upstream data pipelines, clarify source-to-target requirements, identify data quality issues early, and ensure engineered data services are fit for downstream analytics consumption.
5.
Implement analytics quality controls, including data tests, reconciliation checks, metric validation, documentation reviews and controlled releases to improve trust in reports, dashboards and analytical outputs.
6.
Create and maintain clear data product documentation, including model definitions, lineage, business rules, ownership, known limitations and appropriate usage guidance for BI, analytics and operational teams.
7.
Champion forward-looking analytics engineering practices, including reusable data product design, semantic modelling, automation, AI-ready data foundations and the reduction of duplicated logic across reports and teams.
Key Internal Stakeholders & Relationships
* BI and Analytics Team
* Data Engineering Team
* Data Architect
* Data Governance and Data Quality Teams
* Business Product Owners and Operational SMEs
* Technology Operations and Platform Teams
Key External Stakeholders & Relationships
* External data suppliers
* Partner organisations
Essential & Desirable
Essential
• Degree or equivalent experience in data, analytics, computer science or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering or data modelling.
• Advanced SQL skills, with experience building reusable, tested and well-documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models or semantic datasets.
• Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake or equivalent.
• Good understanding of data quality, lineage, governance, privacy and access control in analytical environments.
• Experience working with Power BI or similar BI tools, including how model design affects reporting performance and usability.
• Confident working with data engineers, data architects, BI teams and business stakeholders to deliver trusted data products.
• Experience with dbt, SQLMesh, Fabric semantic models or equivalent analytics engineering tools.
Desirable
• Experience build...
....Read more...
Type: Permanent Location: Tulsa, US-OK
Salary / Rate: Not Specified
Posted: 2026-10-14 14:59:02
