Design, build, and maintain end-to-end ETL/ELT pipelines and data lake/lakehouse solutions on AWS. Optimize Spark jobs, enforce security and compliance, implement data quality and observability, orchestrate workflows, contribute to architecture reviews, and drive CI/CD and infrastructure-as-code for data platforms.
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Software Engineer at JPMorgan Chase within the Consumer and Community Banking – J.P. Morgan Wealth Management organization, you are an integral part of an agile team that designs, enhances, builds, and delivers trusted, market‑leading technology products in a secure, stable, and scalable manner.
Job responsibilities
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Develops secure high-quality production code, and reviews and debugs code written by others
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
- Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
- Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
- Adds to team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Design and implement end-to-end ETL/ELT pipelines on AWS using AWS Glue (PySpark) and/or EMR/EMR Serverless.
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Build and maintain data lakes/lakehouses on Amazon S3 using columnar formats (Parquet/ORC) with effective partitioning, compaction, and schema evolution via Glue Data Catalog.
- Optimize Spark jobs using best practices (predicate pushdown, broadcast joins, AQE, file sizing, caching) and monitor performance with CloudWatch and Spark UI.
- Implement data quality checks and observability , SLAs/SLOs, and lineage/metadata practices & y, scalability, and cost; produce clear documentation and runbooks & Orchestrate workflows with AWS Step Functions and/or Airflow (MWAA), including error handling, retries, and idempotency.
- Enforce security and compliance: IAM least-privilege, KMS encryption, VPC endpoints/private networking, Secrets Manager/Parameter Store, Lake Formation access controls.
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Collaborate on data modeling (dimensional/star, data vault, wide tables) and interface contracts for analytics, ML features, and downstream services.
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Participate in architectural reviews, propose improvements for reliability, scalability, and cost; produce clear documentation and runbooks & Drive CI/CD for data code , infrastructure as code (Terraform or AWS CDK), and automated deployments.
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