Lead data engineering for JPMorganChase’s consumer banking technology team. Design scalable batch and real-time data pipelines, data lake architectures, and end-to-end ingestion-to-analytics solutions using AWS, Spark, Glue, Iceberg, Kafka, and Flink. Build self-healing data operations with agentic AI workflows, establish data architecture standards, and promote secure AI-assisted engineering practices, governance, observability, resiliency, and automated deployment.
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorganChase within the Consumer and community banking technology team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way.
Job responsibilities
Job responsibilities
- Build automated, self-healing data operations using agentic frameworks and AI-driven workflows for intelligent monitoring, optimization, and remediation
- Design and deploy data solutions using AWS services including S3, Lambda, EKS (Elastic Kubernetes Service), and Step Functions
- Implement Infrastructure as Code (IaC) practices for reproducible and scalable deployments
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
- 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.
- Build and optimize scalable data ingestion, transformation, and analytics solutions using Apache Spark, AWS Glue, and Apache Iceberg
- Develop and manage both batch and real-time data pipelines leveraging Apache Kafka and Apache Flink for streaming data processing. Implement Data Lake architectures with efficient data organization, partitioning, and governance strategies
- Design and architect end-to-end data solutions from ingestion to analytics, ensuring scalability, reliability, and performance at enterprise scale
- Define data architecture standards and best practices for batch and real-time data processing pipelines
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with 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 considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
- Experience with modern data engineering practices including DataOps, data observability, and data governance
- Deep expertise in data ingestion, transformation, and analytics using Apache Spark, AWS Glue, Apache Iceberg, and Data Lake architectures
- Proven experience designing and managing batch and real-time data pipelines with Apache Kafka and Apache Flink
- Strong hands-on experience with AWS cloud services including S3, Lambda, EKS, and Step Functions
- Experience building automated solutions using agentic frameworks and AI-driven workflows for intelligent data operations
- Advanced proficiency in programming languages such as Python, Scala, or Java with strong SQL skills
- Demonstrated ability to architect end-to-end data solutions from ingestion to analytics at scale
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