Lead Software Engineer, Data Engineering, Data Technology

JPMorgan Chase

Confirmed live 2 days ago Trusted

Quick summary

Work type
On-site
Location
Columbus, OH
Posted
15 days ago
Freshness
Confirmed live 2 days ago

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How this pay compares to similar roles

Similar $193k
$168k most similar roles pay here $231k

This listing doesn't post a salary. Most similar roles pay $174,337–$211,200.

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 1117 open roles on FindRole.

Listed pay typically runs $186,160–$215,000 across 7 roles with salary data.

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At a glance

TL;DR · Lead Software Engineer, Data Engineering, Data Technology

As a Lead Software Engineer - Data Engineering | Data Technology within the Consumer & Community Banking/Data Products team, you will join an agile team to design and deliver secure, stable, and scalable technology products. You are responsible for developing large-scale ETL data pipelines, building modular Python applications with automated testing, and maintaining distributed processing solutions using PySpark. Your daily work involves managing workflow orchestration via Control-M or Apache Airflow, developing cloud-native solutions on AWS using Glue, Athena, Lambda, and CloudWatch, and managing Snowflake environments alongside Iceberg or Delta Lake architectures. You will champion engineering best practices, CI/CD processes, and AI-assisted development tools to improve code quality. The role requires expertise in DBT, SQL, and Java while addressing complex data challenges within a large-scale enterprise financial services environment to support critical business objectives.

What you'll do

  • Design, develop, and optimize large-scale ETL data pipelines.
  • Build high-quality Python applications using modular code and automated testing.
  • Develop and maintain distributed data processing solutions using PySpark.
  • Implement and support workflow orchestration using Control-M or Apache Airflow.
  • Develop cloud-native solutions leveraging AWS services like Glue, Athena, Lambda, and CloudWatch.
  • Design and manage modern data lake architectures utilizing Iceberg or Delta Lake.
  • Administer and optimize Snowflake environments including streams, tasks, and warehouses.
  • Drive the adoption of AI-assisted engineering practices to improve code quality and delivery speed.

What we're looking for

  • Formal training or certification in software engineering concepts and 5+ years of applied experience with a focus on data engineering.
  • Experience developing, debugging, and maintaining code in a large corporate environment using Python and Java.
  • Hands-on experience with PySpark or other distributed data processing frameworks.
  • Strong expertise in DBT (Data Build Tool) and modern ETL development practices.
  • Experience with workflow orchestration platforms such as Control-M or Apache Airflow (MWAA).
  • Expertise with AWS data services including Glue, Athena, CloudWatch, and Lambda.
  • Experience with Snowflake administration, modern open table formats like Iceberg/Delta Lake, and strong SQL skills.
  • Experience with Kafka, Flink, AI/ML technologies, and mentoring engineers (preferred).

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