Are you ready to shape the future of data engineering at JPMorgan Chase? Join a dynamic team where your unique skills will help build innovative solutions and contribute to a winning culture. You’ll have opportunities for career growth, collaborate with talented professionals, and make a real impact on our business objectives. Your expertise will empower our teams and drive success across the firm. 

As a Data Engineer at JPMorgan Chase as a part of our agile team, you will design and deliver reliable data collection, storage, access, and analytics solutions that are secure, stable, and scalable. You will develop, test, and maintain essential data pipelines and architectures, supporting various business functions to achieve the firm’s goals. Working with us, you will use your skills to drive innovation and help shape our team culture. Together, we focus on excellence, collaboration, and continuous improvement.

Job responsibilities

• Develop workflows and ELT pipelines using Python and Databricks.

• Support review of controls to ensure sufficient protection of enterprise data.

• Implement data security using entitlements frameworks.

• Update logical or physical data models based on new use cases.

• Use SQL frequently and understand NoSQL database

   

  Required qualifications, capabilities, and skills

• Experience with streaming/event-driven processing concepts relevant to data streaming pipelines (event-time vs processing-time, windowing/aggregation, late data handling, replay/backfill, idempotency, backpressure). Hands-on experience in Kafka/Flink or similar tools required.
• Proven ability to implement metadata-driven ingestion frameworks on central data platform and audit-friendly designs  (checksums, batch IDs, extract timestamps, ingestion status tracking) with highly scalable and reusable data products.
• Strong data quality engineering skills (validation rules, standardized transformation patterns, exception handling/quarantine, and completeness checks) and hands-on experience on orchestration tool like Airflow and processing tools Databricks and AWS services like Glue, lambda, EKS and other data analytics and compute services in AWS.

Preferred qualifications, capabilities, and skills

• Direct experience on building/operating telemetry ingestion pipelines (logs/metrics/events) and enabling operational troubleshooting/triage use cases from curated telemetry datasets.
• Experience with enterprise entitlements and distribution constraints, especially where producer/consumer patterns or refined-zone object ownership/access requires guardrails.
• Strong operational engineering background: resiliency design, automation for rebuild/repave recovery, central data platform for producer and consumer layers(similar to datamesh), and disciplined production practices (runbooks, ticketing workflows, version control, and platform readiness expectations).