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 436 open roles on FindRole.

Listed pay typically runs $152,000–$215,000 across 230 roles with salary data.

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At a glance

TL;DR · Lead Software Engineer, Python, Databricks, AWS

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As a Lead Software Engineer at JPMorgan Chase within the External Regulatory Financial Control (ERFC)/Strategic Data team, you will design and develop scalable data processing solutions using Python, PySpark, SQL, and Databricks on AWS. Your daily tasks include creating efficient data pipelines for large-scale financial datasets, developing fact and dimension models for analytics, ensuring data quality and security, and mentoring junior engineers. You will collaborate with cross-functional teams to implement cloud-based data warehouses and support modernization initiatives, while also fostering a culture of innovation and inclusion within the Software Engineering community. This role requires expertise in AWS services, CI/CD methodologies, and regulatory reporting techniques, as well as proficiency in AI tools for development acceleration.

Skills

Python PySpark SQL Databricks AWS S3 ECS SNS/SQS Lambda CI/CD Jenkins Airflow Parquet JSON CSV Avro Delta Lake GitHub Copilot Agile Cloud-based data warehouses

What you'll do

Design and develop scalable data pipelines using Python, PySpark, and Databricks on AWS.
Ensure data quality and security by implementing monitoring and alerting mechanisms for data processing workflows.
Mentor Associate-level engineers to support their technical development and maintain consistent delivery standards.
Collaborate with business stakeholders to transform data into actionable insights for strategic decision-making.
Lead the full Software Development Life Cycle, including requirements gathering, design, testing, deployment, and maintenance.

What we're looking for

Proven experience in data management and large-scale ETL/ELT pipeline development.
Proficiency in Python, PySpark, SQL, and hands-on experience with Databricks and AWS services.
Strong understanding of data quality, security, lineage best practices, and cloud-based data warehouse modernization.
Experience with CI/CD methods, Agile methodologies, and full SDLC processes.
Excellent problem-solving skills, including the ability to investigate and resolve complex data issues.