Lead Data Engineer - ETL Java AI

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within the Consumer & Community Banking Marketing Technology team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems

• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.

• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation

• Lead architecture and hands-on delivery of large-scale data pipelines (ETL/ELT) covering ingestion, transformation, validation, reconciliation, and publishing across curated layers

• Design and operationalize data lake patterns, including partitioning strategies, data quality controls, lineage, governance, and reusable datasets/data products

• Build and optimize distributed processing workloads using Apache Spark and modern storage/file formats (e.g., Parquet, Avro)

• Drive performance tuning across compute and storage (e.g., Spark tuning: shuffle, joins, caching, skew handling; and warehouse tuning where applicable)

• Implement engineering best practices: code quality, automated testing, CI/CD, observability (metrics/logs/traces), security-by-design, and operational readiness

• Build and maintain Java-based services and components that support data workflows (e.g., ingestion services, orchestration helpers, APIs, data access layers)

• Develop REST APIs and integration components to enable downstream consumption and platform interoperability

• Apply modern application engineering practices (clean architecture, SOLID principles, test automation, and secure coding practices)