Lead Data Engineer JPMorganChase
Overview
Lead Data Engineer at JPMorganChase in the Corporate Sector owns data collection storage access and analytics solutions. Build and optimize batch streaming pipelines develop workflow orchestration with Airflow collaborate with cross‑functional teams and mentor engineers. Use Python PySpark SQL and AI tools to improve reliability scalability and security.
What You'll Do11
- 1Deliver data collection storage access and analytics platform solutions in a secure stable scalable way
- 2Build and optimize batch and streaming data pipelines with strong performance fault tolerance and observability
- 3Develop and operate workflow orchestration using Apache Airflow to schedule monitor and manage data movement
- 4Model and transform data for analytics with SQL to support business intelligence and reporting
- 5Write production‑grade PythonPySpark code with disciplined testing performance tuning and maintainable object‑oriented design
- 6Collaborate with analysts data scientists and application teams to translate requirements into technical designs
- 7Own critical data systems by improving reliability scalability security and operational excellence
- 8Mentor junior engineers and shape technical direction through standards reviews and knowledge sharing
- 9Apply reuse‑first AI‑assisted practices such as backup recovery validation and access control review
- 10Ensure traceability auditability and alignment to resiliency and security expectations
- 11Use enterprise‑authorized AI capabilities to accelerate data platform and model design analysis and documentation
Requirements11
- 1Formal training or certification on data engineering concepts and 5+ years applied experience
- 2Demonstrated experience delivering in an agile fast‑paced engineering environment
- 3Hands‑on professional experience actively coding as a data engineer
- 4Strong software engineering fundamentals including system design data structures object‑oriented programming testing strategies and end‑to‑end development lifecycle
- 5Understanding of creating and maintaining data models conceptual logical and physical including dimensional and normalized modeling
- 6Hands‑on experience building and operating cloud‑based data platforms using AWS Google Cloud or Azure
- 7Experience with large‑scale distributed data processing and performance tuning
- 8Hands‑on experience with modern data warehousing lakehouse technologies
- 9Strong SQL skills and experience with SQL‑based transformation tooling
- 10Experience designing and operating orchestration pipelines using Airflow or similar tools
- 11Demonstrated experience using enterprise‑authorized AI capabilities within the work environment to support data engineering workflows
Salary Insight
$142 - $185k per year
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