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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.
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Lead Software Engineer: Data Engineering joins the Employee Platforms team as a core technical contributor responsible for the architecture and hands-on delivery of secure, stable, and scalable data engineering solutions. This role involves designing and building data platforms and pipelines across ingestion, transformation, curation, and consumption stages. The engineer will develop high-quality production code, create reusable Python or Scala libraries, and manage operational excellence including monitoring, alerting, and performance optimization. Key technologies include AWS, Databricks, Spark, and Delta, alongside the integration of enterprise-authorized GenAI tools like GitHub Copilot and Claude Code to accelerate development. The position requires expertise in modern lakehouse patterns, streaming architectures, and CI/CD pipelines. The role focuses on solving complex data engineering problems by building robust infrastructure while mentoring engineers and ensuring high standards for security and reliability within an agile environment.
Architect and deliver data platforms and pipelines using Spark and Delta on AWS and Databricks.
Develop high-quality production code in Python or Scala while performing peer code reviews.
Build reusable libraries, frameworks, pipeline templates, and automation tools for the engineering team.
Manage operational excellence including data quality, monitoring, alerting, incident triage, and cost optimization.
Drive SDLC standards such as CI/CD, testing strategies, secure coding, and resiliency patterns.
Lead and mentor engineers while partnering with product and security teams to deliver roadmaps.
Integrate enterprise-authorized GenAI tools to accelerate refactoring, test generation, and documentation.
Identify and automate the remediation of recurring issues to improve system stability.
What we're looking for
Formal training or certification in data engineering concepts with architecture and production ownership and 5+ years of experience.
Hands-on experience delivering system design, application development, testing, and operational stability.
Strong Python coding skills with deep experience using Databricks and Spark.
Demonstrated experience leading the use of approved AI-assisted software development tools for coding, review, and troubleshooting.
Proficiency in all aspects of the Software Development Life Cycle including CI/CD, application resiliency, and security.
Strong experience with AWS and modern lakehouse and data platform patterns.
Proven technical leadership through architecture decisions, mentoring, and cross-team influence.
Experience with streaming architectures, including event-driven ingestion and near-real-time processing (preferred); AWS or Databricks certifications (preferred).
Java
Python
Scala
AWS
Apache Spark
Databricks
AI-assisted engineering
CI/CD
Agile
Big Data
Cloud Native
Data Visualization
Business Intelligence
System Design
Automated Testing
Java
Python
Scala
AWS
Apache Spark
Databricks
AI-assisted engineering
CI/CD
Agile
Big Data
Cloud Native
Data Visualization
Business Intelligence
System Design
Automated Testing