Lead Software Engineer, Data Technology, Data Engineering

JPMorgan Chase

Confirmed live yesterday Trusted

Quick summary

Work type: On-site
Location: OH
Posted: 17 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $197k

$174k most similar roles pay here $231k

This listing doesn't post a salary. Most similar roles pay $182,200–$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 Technology, Data Engineering

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As a Lead Software Engineer - Data Technology | Data Engineering 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 will be responsible for building high-quality Python applications with modular code, developing distributed data processing solutions using PySpark, and designing large-scale ETL pipelines. Your daily work involves managing workflow orchestration via Control-M or Apache Airflow, implementing cloud-native solutions on AWS using Glue, Athena, Lambda, and CloudWatch, and managing Snowflake environments alongside Iceberg or Delta Lake architectures. You will utilize DBT for data development and leverage AI-assisted tools to improve code quality and delivery speed. The role focuses on solving complex technical challenges within a large-scale enterprise data environment specifically tailored for the financial services industry.

Skills

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

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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