Lead Software Engineer, Data Engineering

JPMorgan Chase

Confirmed live today Low trust

Quick summary

Work type: On-site
Location: Columbus, OH
Employment: Full-time
Posted: 34 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $160k

$116k $207k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $126,800–$192,850.

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 1226 open roles on FindRole.

Listed pay typically runs $156,750–$215,000 across 5 roles with salary data.

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At a glance

TL;DR · Lead Software Engineer, Data Engineering

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As a Lead Software Engineer - Data Engineering | Data Technology, you will join the Consumer & Community Banking/Data Products team to design and deliver trusted, scalable technology products. You will be responsible for designing, developing, and optimizing large-scale ETL data pipelines and building high-quality Python applications using modular code and automated testing. Your daily work involves developing distributed data processing solutions using PySpark, implementing workflow orchestration via Control-M or Apache Airflow, and developing cloud-native solutions using AWS services like Glue, Athena, Lambda, and CloudWatch. You will manage modern data lake architectures utilizing Iceberg or Delta Lake and administer Snowflake environments. Key technical requirements include expertise in DBT, SQL, and Java, while ensuring secure, compliant software delivery within a large-scale enterprise data environment.

Skills

Python Java PySpark SQL AWS Glue Athena Lambda CloudWatch Snowflake Apache Airflow Control-M DBT Iceberg Delta Lake ETL CI/CD Kafka Flink LLMs GitHub Copilot

What you'll do

Design, develop, and optimize large-scale ETL data pipelines.
Build high-quality Python applications using modular code, reusable components, 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 and Delta Lake.
Administer and optimize Snowflake environments, including streams, tasks, roles, and warehouses.
Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery.

What we're looking for

Formal training or certification in software engineering concepts and 5+ years of applied experience in data engineering.
Experience developing, debugging, and maintaining code in large corporate environments using Python and Java.
Hands-on experience with PySpark or other distributed data processing frameworks.
Strong expertise in DBT 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.
Knowledge of modern open table formats such as Iceberg and/or Delta Lake.
Experience with Snowflake administration, development, and strong SQL skills.

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