Build and maintain scalable ETL and data pipelines using Python, PySpark, AWS Glue, and S3. Ensure data quality, reliability, monitoring, and performance through validation and optimization. Collaborate with technical teams to deliver documented datasets and interfaces, deploy and monitor ML systems, and implement reproducible training pipelines, low-latency inference services, infrastructure as code, secure networking, and least-privilege access. Use AI-assisted development tools responsibly while applying software engineering, testing, automation, and security standards.
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a Software Engineer III at JPMorganChase within the Consumer & Commercial Banking you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Design, build, and maintain scalable ETL/data pipelines using Python and PySpark on AWS Glue and S3.
- Ensure data quality, reliability, and performance via validation checks, monitoring, and Spark/Glue job optimization.
- Collaborate with upstream/downstream teams to gather requirements, troubleshoot issues, and deliver well-documented datasets/interfaces.
- Own end-to-end hands-on technical delivery (100%), following engineering standards; Java exposure is a plus.
- Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
- 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.
- A reproducible training pipeline with automated validation and promotion to production.
- A low-latency inference service with monitoring, alerting, and drift detection.
- Infrastructure-as-code for ML environments with secure networking and least-privilege IAM
Required qualifications, skills, and capabilities
- 3+ years (or equivalent) building and deploying ML systems in production.
- Strong programming skills in Python and solid software engineering fundamentals (APIs, testing, design patterns).
- Strong hands-on experience with Python and PySpark for building production-grade ETL pipelines.
- Hands-on AWS experience, including several of: S3, IAM, VPC, EC2, ECR, ECS/EKS, Lambda, CloudWatch, CloudFormation/Terraform.
- Experience with data processing tools (e.g., Spark, AWS Glue, Athena, EMR) and SQL.
- Practical knowledge of deploying/serving models (REST/gRPC), performance tuning, and monitoring.
- Solid knowledge of ETL concepts, data modeling basics, and handling large-scale batch/incremental processing.
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
Preferred qualifications
- Hands-on experience with AWS Glue (Jobs, Crawlers, Data Catalog) and Amazon S3.
- Strong SQL skills and experience implementing data quality / observability practices (reconciliations, validation checks, monitoring/alerting).
- Experience with CI/CD for data pipelines, Git-based workflows, and automated testing.
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