Lead Software Engineer - Data Engineer

The Lead Software Engineer - Data Engineer role within JPMorgan Chase’s Cloud Financial Management Technology group provides core technical leadership on an agile team. The position focuses on building and delivering trusted, secure, stable, and scalable technology products, with emphasis on production data pipelines on AWS and secure, AI-assisted engineering practices.

Responsibilities

• Design, develop, and troubleshoot software solutions, applying creative thinking to go beyond conventional approaches when building solutions or breaking down complex technical problems.
• Deliver secure, high-quality production code and perform code reviews and debugging for work produced by other team members.
• Drive adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, including AI-assisted code review/refactoring, test strategy acceleration, and incident or root-cause analysis support.
• Establish and reinforce consistent validation standards across secure coding, peer review, and automated testing, while promoting reuse of effective patterns across the team.
• Apply knowledge of Software Development Life Cycle toolchain capabilities, including enterprise-authorized AI-assisted development and automation, to increase the value realized from automation.
• Identify opportunities to remove or automate remediation of recurring issues to strengthen operational stability for applications and systems.
• Lead evaluation sessions with external vendors, startups, and internal teams to probe architectural designs, technical credentials, and applicability within existing systems and information architecture.
• Lead communities of practice across Software Engineering to promote awareness and adoption of new and leading-edge technologies.
• Contribute to a team culture grounded in diversity, opportunity, inclusion, and respect.

Requirements

• Formal training or certification in software engineering concepts, plus 5+ years of applied experience.
• Proficiency in system design, testing, and operational ownership.
• Advanced Python and cloud-native engineering on AWS, including experience with IAM, VPC, KMS, and CloudWatch.
• Proven delivery of production ETL/ELT pipelines on AWS (batch and/or streaming), using AWS Glue, Amazon EMR, AWS Lambda, and orchestration via Amazon MWAA (Airflow) and/or AWS Step Functions.
• Strong data engineering fundamentals, including CDC or incremental processing, backfills, idempotency, late-arriving data handling, and schema evolution.
• Experience with AWS analytics and storage services (Amazon S3, Amazon Redshift, Amazon Athena, and AWS Lake Formation/Glue Data Catalog), along with streaming and messaging tools such as Amazon Kinesis and Amazon MSK.
• Knowledge of data reliability practices, including data quality controls, monitoring and alerting, CI/CD for pipelines (CodePipeline/CodeBuild), performance and cost optimization, and security or governance compliance (CloudTrail, least-privilege access).
• Demonstrated experience leading effective use of approved AI-assisted software development tools for coding, code review, test acceleration, and troubleshooting, including the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and coaching engineers on safe and compliant adoption within delivery practices.
• Practical experience using Large Language Models (LLMs) to accelerate advanced coding workflows.

Technologies

• Python, AWS, IAM, VPC, KMS, CloudWatch
• AWS Glue, Amazon EMR, AWS Lambda, Amazon MWAA (Airflow), AWS Step Functions
• CDC/incremental processing, Amazon S3, Amazon Redshift, Amazon Athena
• AWS Lake Formation/Glue Data Catalog, Amazon Kinesis, Amazon MSK
• CodePipeline, CodeBuild, CloudTrail
• Large Language Models (LLMs)

About the Team

Corporate Functions at JPMorgan Chase span a range of areas including finance and risk, human resources, and marketing. These corporate teams support company success by helping ensure businesses, clients, customers, and employees are set up to perform effectively.