Lead Data Engineer

Make a real impact as you shape the future of secure data engineering and analytics at one of the world's most influential companies.

As a Lead Data Engineer at JPMorganChase within the Cybersecurity and Technology Controls organization, you will design, build, and operate scalable, governed data pipelines, models, and analytics products that enable Cyber Operations decision-making.

You will lead the implementation of tamper-evident, audit-defensible data solutions across multiple domains with a strong focus on data quality, lineage, observability, and secure access.

You'll primarily leverage SQL and Python across modern data warehousing and lakehouse platforms, integrating security telemetry and threat intelligence at scale, and partnering with stakeholders to turn data into actionable insights.

We value diversity, equity, and inclusion, and we're looking for a leader who will strengthen that culture.

Job Responsibilities

- Lead the design, build, and operation of secure, scalable ETL/ELT pipelines, dimensional and semantic data models (including canonical dimensions, fact tables, and slowly changing dimensions), and lakehouse/warehouse structures supporting Cyber Operations analytics and reporting

- Architect modular SQL/dbt transformations from raw ingestion through conformed marts, selecting incremental and materialization strategies aligned to cost and SLA targets.

- Establish and enforce data engineering best practices for data quality (validation, profiling), lineage, cataloging, observability, and modeling standards (naming conventions, layers, contracts) to drive consistency, interoperability, and trust across teams; define SLAs for freshness, completeness, and accuracy.

- Design and deliver stakeholder-ready analytics outputs-including curated datasets, dashboards, metrics layers, and semantic models-translating cyber telemetry and threat intelligence into actionable KPIs for incident response, posture management, and leadership reporting; iterate with stakeholders to codify KPI definitions and implement change-control for metrics.

- Build and maintain pipelines ingesting data from multiple Cyber Intelligence vendors and internal security telemetry sources; normalize and enrich data using scalable transformation frameworks.

- Collaborate with data consumers (Cyber Operations, product, platform, application owners) to understand analytical use cases, define data contracts, and prioritize high-impact deliverables; contribute to the analytics data model roadmap, aligning models and metrics to business goals and stakeholder priorities; lead cross-functional initiatives that improve data reliability, observability, and time-to-insight.

- Implement orchestration and CI/CD for data workflows; automate unit, data quality, and contract tests; manage versioned, reproducible deployments; implement secure-by-design patterns including encryption, tokenization, fine-grained access controls, and audit-evident logging across pipelines and st...