ETL Java AI Lead Engineer

JPMorgan Chase & Co.; Columbus (OH); On-site; USD 140,000 - 180,000; Full time

Job summary

JPMorganChase in Columbus, OH, seeks a Lead Software Engineer to advance its Consumer & Community Banking Marketing Technology team. You will be a core technical contributor within an agile environment, delivering secure, scalable technology products across multiple business functions.

You will lead architecture and hands-on delivery of large-scale ETL/ELT pipelines, design data lake patterns, optimize Spark workloads, and implement best practices, CI/CD, and observability while building Java

Qualifications

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Demonstrated experience leading effective use of AI-assisted software development tools with the ability to set team expectations for validating outputs for correctness, performance, and security.
• 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 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 including data quality, metadata, and governance
• Experience working with cloud data warehouses such as Snowflake or equivalent
• Ability to lead technical discussions and drive delivery in cross-functional environments

Responsibilities

• Executes creative software solutions, design, development, and troubleshooting with ability to think beyond routine
• Drives team adoption of AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes
• Applies knowledge of SDLC tools to improve automation and value
• Lead architecture and hands-on delivery of large-scale data pipelines (ETL/ELT) across curated layers
• Design and operationalize data lake patterns with quality controls and governance
• Build and optimize distributed processing workloads using Spark and modern file formats
• Drive performance tuning across compute and storage and warehouse tuning
• Implement engineering best practices: code quality, automated testing, CI/CD, observability, security-by-design
• Build and maintain Java-based services and components supporting data workflows
• Develop REST APIs and integration components for platform interoperability
• Apply modern application engineering practices (clean architecture, SOLID, test automation, secure coding)

Skills

Java

Python

Data engineering

Spark

Cloud data platforms

Education

Bachelor's degree in Computer Science

Tools

Apache Spark

Parquet

Avro

Snowflake

Airflow

Dagster

Job description

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.

Job 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)

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
• 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

Preferred qualifications, capabilities, and skills

• 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.