Software Engineer III - Data Engineer
JPMorgan Chase · Posted Oct 12
Global banking, investment, credit card, mortgage, and asset management services
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About the role
The Cloud Data Engineering team at JPMorgan Chase modernizes cloud data engineering by delivering trusted lakehouse products and shaping responsible AI-assisted engineering practices. The team builds scalable data engineering products on AWS using Databricks and lakehouse architecture for batch and streaming workloads. This Software Engineer III role designs, builds, and operates ETL/ELT pipelines and leads the adoption of AI-assisted engineering practices across the ETL lifecycle.
What you will do
- Design scalable extract-transform-load (ETL) and extract-load-transform (ELT) pipelines on AWS using Databricks, PySpark, Spark, Delta Lake, AWS Glue, SQL, and lakehouse patterns for batch and streaming workloads.
- Build ingestion, transformation, enrichment, calculation, allocation, reporting, and publishing pipelines that produce maintainable, well-modeled data products.
- Develop production-grade Python, PySpark, and SQL with disciplined engineering practices including testing, peer review, performance tuning, resiliency, and secure coding.
- Operate orchestration and scheduling workflows using Airflow (or equivalent), including dependency management, retries, backfills, service level agreement monitoring, and operational runbooks.
- Implement continuous integration and continuous delivery practices with automated quality gates (linting, unit and integration tests, security scans, data-quality checks, schema validation, and release controls).
- Build and maintain infrastructure as code using Terraform and/or AWS CloudFormation, and create architecture, design, lineage, and operational artifacts that meet scalability, performance, cost, resiliency, governance, and security requirements.
- Lead adoption of AI-assisted engineering practices across the ETL lifecycle (Copilot-assisted development, Claude-enabled refactoring and troubleshooting, and reusable AI skills and task-oriented agents) and integrate approved developer tools with Jira, source control, documentation, observability, incident triage, and release readiness—ensuring controls for human review, secrets protection, and safeguards for personally identifiable information and payment card information.
Skills used in this role
What the employer is looking for
- 8 years of hands-on data engineering or software engineering experience delivering production data platforms and pipelines.
- Hands-on experience building data products using Databricks, Spark, Delta Lake, and lakehouse architectures on AWS.
- Advanced proficiency in Python, PySpark, and SQL, with demonstrated ability to write clean, testable, maintainable, production-grade code.
- Hands-on experience with AWS Glue and cloud-native ETL patterns, including jobs, cataloging, identity and access management security, monitoring, and operational support.
- Experience with Airflow (or equivalent orchestration), including workflow design, scheduling, backfills, dependency management, monitoring, and recovery.
- Hands-on Apache NiFi experience for ingestion design, flow operationalization, source onboarding, and reliability practices.
- Demonstrated experience with disciplined delivery practices, including infrastructure as code (Terraform and/or AWS CloudFormation), automated testing and quality gates, release controls, agile delivery, DevOps practices, data governance, resiliency engineering, secure-by-design implementation, AI-assisted workflows (including GitHub Copilot and Claude) with human review, reusable AI skills/prompts/agents, and controls for sensitive data (personally identifiable information and payment card information), secrets management, access control, auditability, and secure logging.
Preferred qualifications
- Experience implementing agentic automation patterns for incident triage, deployment validation, data-quality checks, lineage generation, developer enablement, or operational workflows.
- Experience integrating AI agents with Jira, source control systems, documentation platforms, Databricks, observability platforms, or internal developer tooling.
- Familiarity with data mesh concepts, Unity Catalog, data contracts, metadata management, and modern analytics and business intelligence ecosystems (for example, ThoughtSpot), including streaming and event-driven ingestion (for example, Kafka) and enterprise observability tooling.
- AWS and/or Databricks certifications (for example, AWS Solutions Architect, AWS Developer Associate, AWS Data Analytics Specialty, or Databricks Data Engineer certification) and experience mentoring engineers and leading standards for AI-assisted data engineering delivery.
Benefits and support
- Compensation and benefits are detailed in the job posting
About JPMorgan Chase
JPMorgan Chase & Co. is one of the oldest, largest, and most diversified financial services institutions in the world. Operating globally through the J.P. Morgan and Chase brands, the firm provides a comprehensive range of financial solutions including investment banking, consumer and commercial banking, transaction processing, and asset management. Serving millions of consumers and many of the world's most prominent corporate, institutional, and government clients, the company combines deep market expertise with extensive technological investments.
- Industry
- Banking
- Company size
- 320000+ employees
- Founded
- 1799
- Location
- New York, New York, USA
- Funding stage
- Public Company
Leadership
Chairman and Chief Executive Officer
Chief Financial Officer
Chief Operating Officer
Global Chief Information Officer
CEO, Asset & Wealth Management
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