ETL Java AI Lead Engineer

JPMorgan Chase

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Work type: On-site
Location: Columbus, OH
Posted: 3 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Similar $189k

$135k most similar roles pay here $235k

This listing doesn't post a salary. Most similar roles pay $157,200–$220,187.

Based on 240 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1151 open roles on FindRole.

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At a glance

TL;DR · ETL Java AI Lead Engineer

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The ETL Java AI Lead Engineer joins the Consumer & Community Banking Marketing Technology team as a core technical contributor in an agile environment. This role involves leading the architecture and hands-on delivery of large-scale data pipelines, designing data lake patterns, and building distributed processing workloads. The engineer will develop Java-based services, REST APIs, and integration components while driving the adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed. Key technical requirements include proficiency in Java and Python, extensive experience with Apache Spark, and knowledge of Parquet and Avro formats. The role focuses on solving complex data engineering problems including ingestion, transformation, and validation within a regulated environment. Essential skills include performance tuning, CI/CD, and implementing secure coding practices to ensure stable, scalable technology products for the firm's business objectives.

Skills

Java Python Apache Spark ETL ELT REST APIs CI/CD Snowflake Kafka Delta Lake Apache Iceberg Airflow Parquet Avro Data Lake Gen AI Observability Distributed Processing

What you'll do

Design and deliver large-scale ETL/ELT pipelines covering ingestion, transformation, validation, and publishing across curated layers.
Build and optimize distributed processing workloads using Apache Spark and modern storage formats like Parquet and Avro.
Drive the adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed.
Design and operationalize data lake patterns including partitioning strategies, data quality controls, and governance.
Develop Java-based services, REST APIs, and integration components to support data workflows and platform interoperability.
Implement engineering best practices including CI/CD, automated testing, security-by-design, and observability.
Perform performance tuning for compute and storage across Spark jobs and data warehouses.

What we're looking for

Formal training or certification on software engineering concepts and 5+ years of applied experience.
10+ years of software engineering experience with strong depth in data engineering and big data platforms.
Strong hands-on development experience in Java and proficiency in at least one data-focused language such as Python.
Demonstrated experience leading the use of approved AI-assisted software development tools while ensuring security, performance, and correctness.
Proven experience designing and building robust ETL/ELT pipelines and data integration frameworks.
Strong experience with Apache Spark, distributed processing concepts, and data storage formats like Parquet and Avro.
Solid knowledge of data lake/lakehouse patterns, including Medallion architecture, data quality, and governance.
Experience with cloud data warehouses such as Snowflake or equivalent.
Experience with orchestration tools (preferred).
Exposure to streaming processing and table formats like Delta Lake or Iceberg (preferred).
Experience in regulated environments and production operations mindset (preferred).

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