This listing doesn't post a salary. Most similar roles pay
$192,050–$223,750.
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
1117 open roles
on FindRole.
Listed pay typically runs
$186,160–$215,000
across 7 roles with salary data.
As a Lead Software Engineer - Data & AI Platform Engineer within the Commercial & Investment Banking – Data Analytics – Payments Technology team, you will serve as a core technical contributor in an agile environment. You will design and maintain scalable data pipelines and ETL/ELT workflows for batch and real-time processing while developing platform components like data cataloging, quality frameworks, and semantic layers. Your daily work involves implementing data modeling strategies, producing high-quality code, and leading the development of Agentic Autonomous Lakehouse capabilities to automate governed self-service operations. You will utilize Spark, Airflow, Kafka, Flink, Python, Java, and SQL to build production-grade systems. Technical expertise includes Databricks, Kubernetes, AWS services, and advanced technologies such as LLMs, RAG architectures, vector databases, and infrastructure tools like Terraform to solve complex data governance and scalability challenges.
Design, build, and maintain scalable data pipelines and ETL/ELT workflows using Spark, Airflow, Kafka, and Flink.
Develop data platform components including cataloging, quality frameworks, and semantic layers with embedded governance.
Implement data modeling strategies such as fact and dimensional models to support analytics and reporting.
Translate complex business requirements from stakeholders into production-grade technical solutions.
Write, review, and debug high-quality production code while ensuring system stability and security.
Lead the development of Agentic Autonomous Lakehouse capabilities for automated self-service pipeline provisioning.
Evaluate external vendors and internal technologies to determine architectural fit and applicability.
Lead communities of practice to drive adoption of emerging software engineering technologies.
What we're looking for
Formal training or certification in software engineering concepts and 5+ years of applied experience.
Professional experience in software engineering or data platform development including system design, application development, testing, and operational stability.
Advanced proficiency in Python, Java, and SQL.
Hands-on experience with distributed data processing frameworks like Apache Spark and Flink.
Experience designing and operating data pipelines on Databricks using orchestration tools such as Apache Airflow.
Proficiency with cloud data services including AWS S3, Glue, Redshift, Athena, EMR, Lake Formation, or equivalent.
Experience engineering production-grade data platforms on Kubernetes with open catalog integration for scalable discovery and governance.