Lead Data Engineer - ETL Java AI
Salary not listed • Columbus, OH, United States • Full-time
Posted 15d ago
No salary is listed. Many US states require employers to post a pay range, so ask about pay early.
About the job
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 & Community Banking Marketing 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Responsibilities
- Execute creative software solutions, design, development, and technical troubleshooting.
- Drive team adoption of AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes.
- Apply knowledge of Software Development Life Cycle tools, including AI-assisted development and automation capabilities.
- Lead architecture and hands-on delivery of large-scale data pipelines (ETL/ELT) covering ingestion, transformation, validation, reconciliation, and publishing.
- Design and operationalize data lake patterns, including partitioning strategies, data quality controls, lineage, governance, and reusable datasets/data products.
- Build and optimize distributed processing workloads using Apache Spark and modern storage/file formats (e.g., Parquet, Avro).
- Drive performance tuning across compute and storage (e.g., Spark tuning, warehouse tuning).
- Implement engineering best practices: code quality, automated testing, CI/CD, observability, security-by-design, and operational readiness.
- Build and maintain Java-based services and components that support data workflows.
- Develop REST APIs and integration components to enable downstream consumption and platform interoperability.
- Apply modern application engineering practices (clean architecture, SOLID principles, test automation, and secure coding practices).
Requirements
- 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.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations and secure handling of inputs/outputs.
- 10+ years of software engineering experience, with strong depth in data engineering/big data platforms.
- Strong hands-on development experience in Java plus proficiency in at least one data-focused language such as Python.
- Proven experience designing and building robust ETL/ELT pipelines and data integration frameworks.
- Strong experience with Apache Spark and distributed processing concepts.
- Strong understanding of data storage serialization and formats such as Parquet and Avro.
- Solid knowledge of data lake/lakehouse concepts and patterns.
- Experience working with cloud data warehouses such as Snowflake or equivalent.
- Ability to lead technical discussions, communicate clearly to varied stakeholders, and drive delivery in cross-functional environments.