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Based on 240 similar postings.
Employer
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
1260 open roles
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As a Lead Software Engineer: DevOps & Infrastructure within the Employee Platforms Workforce Experience Tech team, you will join an agile group focused on designing and delivering secure, stable, and scalable software components. You will execute creative software solutions, develop high-quality production code, and perform technical troubleshooting for complex systems. Your daily work involves designing and operating ETL/ELT data pipelines using Python, SQL, and distributed data-processing technologies. You will utilize Databricks, Apache Spark, PySpark, Delta Lake, and Lakeflow pipelines within a lakehouse architecture. Key responsibilities include driving team adoption of AI-assisted engineering practices, managing data governance, and optimizing query performance. You will also lead evaluation sessions with vendors to probe architectural designs and ensure operational stability across the firm's technical product landscape.
Design, develop, and troubleshoot complex software components for secure and scalable production systems.
Write high-quality production code and perform code reviews and debugging for other team members.
Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed.
Establish consistent validation standards for secure coding, peer reviews, and automated testing.
Identify and automate the remediation of recurring issues to improve overall operational stability.
Lead evaluation sessions with external vendors and internal teams to probe architectural designs and technical credentials.
Integrate data from relational databases, APIs, message streams, and cloud-based storage platforms.
Develop and operate ETL/ELT data pipelines using Python, SQL, and distributed data-processing technologies.
What we're looking for
Formal training or certification in software engineering concepts and 5+ years of applied experience.
Hands-on experience in system design, application development, testing, and operational stability.
Hands-on experience designing, developing, and operating ETL/ELT data pipelines using Python, SQL, and distributed data-processing technologies.
Strong understanding of data warehousing concepts, including dimensional modeling, star and snowflake schemas, partitioning, and query optimization.
Experience developing production data solutions with Databricks, Apache Spark or PySpark, Delta Lake, Lakeflow pipelines, and lakehouse architecture.
Knowledge of data governance, lineage, access controls, schema management, data quality, and production monitoring.
Experience developing, debugging, and maintaining code in a large corporate environment using modern programming languages and database query languages.
Strong knowledge of SDLC, Agile delivery, CI/CD practices, application resiliency, and secure software development principles.