ETL Java AI Lead Engineer

JPMorgan Chase & Co.•Columbus, OH

About The Position

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.

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 (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
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 (fault tolerance, partitioning, performance tuning)
Strong understanding of data storage serialization and formats such as Parquet and Avro
Solid knowledge of data lake/lakehouse concepts and patterns (e.g., Medallion architecture), including data quality, metadata, and governance considerations
Experience working with cloud data warehouses such as Snowflake (loading/unloading patterns, performance basics) or equivalent
Ability to lead technical discussions, communicate clearly to varied stakeholders, and drive delivery in cross-functional environments

Nice To Haves

Experience with orchestration and workflow scheduling tools (e.g., Airflow, Dagster, Control-M, etc.)
Exposure to streaming/event-driven processing (e.g., Kafka, Spark Structured Streaming) and incremental processing patterns (CDC, upserts)
Experience with table formats / ACID layers (e.g., Delta Lake, Apache Iceberg, Hudi).
Strong production operations mindset (on-call practices, SLAs/SLOs, observability, incident response).
Experience in regulated environments (risk, audit, compliance, privacy, PII handling).
Experience with cost optimization / FinOps practices for data platforms.
Experience with working with Agentic AI / Gen AI solutions and capabilities will be an added advantage.

Responsibilities

Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
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
Lead architecture and hands-on delivery of large-scale data pipelines (ETL/ELT) covering ingestion, transformation, validation, reconciliation, and publishing across curated layers
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: shuffle, joins, caching, skew handling; and warehouse tuning where applicable)
Implement engineering best practices: code quality, automated testing, CI/CD, observability (metrics/logs/traces), security-by-design, and operational readiness
Build and maintain Java-based services and components that support data workflows (e.g., ingestion services, orchestration helpers, APIs, data access layers)
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)