JOB DESCRIPTION
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
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Design, build, and maintain scalable ETL/data pipelines using Python and PySpark on AWS Glue and S3.
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Ensure data quality, reliability, and performance via validation checks, monitoring, and Spark/Glue job optimization.
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Collaborate with upstream/downstream teams to gather requirements, troubleshoot issues, and deliver well-documented datasets/interfaces.
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Own end-to-end hands-on technical delivery (100%), following engineering standards; Java exposure is a plus.
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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.
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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.
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A reproducible training pipeline with automated validation and promotion to production.
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A low-latency inference service with monitoring, alerting, and drift detection.
Infrastructure-as-code for ML environments with secure networking and least-privilege IAM
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Required qualifications, skills, and capabilities
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3+ years (or equivalent) building and deploying ML systems in production.
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Strong programming skills in Python and solid software engineering fundamentals (APIs, testing, design patterns).
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Strong hands-on experience with Python and PySpark for building production-grade ETL pipelines.
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Hands-on AWS experience, including several of: S3, IAM, VPC, EC2, ECR, ECS/EKS, Lambda, CloudWatch, CloudFormation/Terraform.
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Experience with data processing tools (e.g., Spark, AWS Glue, Athena, EMR) and SQL.
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Practical knowledge of deploying/serving models (REST/gRPC), performance tuning, and monitoring.
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Solid knowledge of ETL concepts, data modeling basics, and handling large-scale batch/incremental processing.
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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.
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Preferred qualifications
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Hands-on experience with AWS Glue (Jobs, Crawlers, Data Catalog) and Amazon S3.
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Strong SQL skills and experience implementing data quality / observability practices (reconciliations, validation checks, monitoring/alerting).
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Experience with CI/CD for data pipelines, Git-based workflows, and automated testing.
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