Location: Wilmington, DE, United States

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 outputsincluding curated datasets, dashboards, metrics layers, and semantic modelstranslating 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 storage.

- Troubleshoot pipeline and data incidents (schema drift, late or out-of-order data, performance regressions), perform root cause analysis, and implement preventative controls.

- Write clean, maintainable SQL and Python following best practices and coding standards (including dbt style guides and tests); lead reviews and mentor engineers on data modeling, pipeline design, and analytics engineering.

- Influence platform and tooling decisions with clear tradeoffs; evaluate emerging technologies and practices in data engineering, analytics engineering, cloud computing, and cybersecurity to continuously improve the team's capabilities.

 

Required Qualifications, Capabilities, and Skills

- Formal training or certification with 5+ years in professional software/data engineering roles in cloud-based environments.

- Strong proficiency in SQL, including dimensional and semantic modeling, performance optimization, and building transformation frameworks within data warehouses or lakehouses; hands-on experience with dbt or comparable tools strongly preferred.

- Proven experience designing, building, and operating ETL/ELT pipelines, including orchestration (e.g., Airflow, Dagster, AWS Step Functions) and data quality frameworks (e.g., dbt tests, Great Expectations).

- Proficiency in Python for data engineering (e.g., Pandas, PySpark), automated testing, and packaging.

- Experience delivering analytics outputs (curated datasets, dashboards, metrics layers) and partnering with stakeholders to define KPIs, data contracts, and acceptance criteria; familiarity with BI tooling is a plus.

- Strong understanding of API protocols and standards, including REST and GraphQL, for data ingestion and service integration.

- Demonstrated professional use of AI coding assistants within the SDLC and hands-on experience enabling retrieval-augmented LLM or agent workflows with governed data.

- Experience with CI/CD for data code, Git/GitHub workflows, containerization, and Infrastructure as Code (e.g., Terraform) for data platforms.

- Solid understanding of agile methodologies and DataOps/DevOps best practices, including pipeline resiliency, security, and test-driven development.

- Excellent problem-solving skills, attention to detail, and ability to work independently or as part of a team; strong communication and interpersonal skills to collaborate with stakeholders, provide training, and solicit feedback.

 

Preferred Qualifications, Capabilities, and Skills

 

- Hands-on experience with AWS data services such as S3, Glue, Athena, EMR, Lambda, EC2, SQS, RDS, DynamoDB, and Lake Formation; experience with Trino/Presto and open table formats such as Apache Iceberg, Delta, or Parquet is a plus.

- Experience with streaming and large-scale telemetry (e.g., Kinesis, Kafka, Splunk integrations) and cost/performance optimization for high-volume workloads.

- Knowledge of JavaScript frameworks (React preferred) for lightweight internal data apps or analytics front-ends.

- Data Science experience, especially in feature engineering, model monitoring, or ML-ready dataset preparation.

- AWS certification (e.g., AWS Certified Solutions Architect, AWS Certified Data Analytics Specialty) preferred.

 

Join a high-performing team building secure, reliable data platforms and analytics solutions that power Cyber Operations and drive measurable outcomes across the firm.