at J.P. Morgan
Bulge Bracket Investment BanksPosted 13 days ago
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**Lead Data Engineer - JPMorganChase, Jersey City, NJ** **Lead and deliver** scalable, secure data collection, storage, and analytics solutions. **Build & optimize** ETL/ELT pipelines with high performance and fault tolerance. **Develop & operate** workflow orchestration (Apache Airflow) and **model/transform** data for analytics using SQL. **Code** in Python/PySpark with disciplined testing and maintainable design. **Collaborate** with cross-functional teams to convert requirements into technical designs and solutions. **Mentor junior engineers** and **influence team's technical direction**. **Own** and **improve** critical data systems, ensuring operational excellence. 5+ years' applied experience in data engineering required, with strong software engineering fundamentals and distributed data processing experience. Proven cloud-based data platform, SQL, and data modeling skills essential. Experience with AI-assisted tools and validation practices desirable. Join an agile, collaborative environment at a global leading financial institution.
- Compensation
- Not specified USD
- City
- Jersey City
- Country
- United States
Currency: $ (USD)
Full Job Description
Location: Jersey City, NJ, United States
Join us as we embark on a journey of collaboration and innovation, where your unique skills and talents will be valued and celebrated. Together we will create a brighter future and make a meaningful difference.
As a Lead Data Engineer at JPMorganChase within the Corporate Sector, you are an integral part of an agile team that works to enhance, build, and deliver data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. As a core technical contributor, you are responsible for maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firms business objectives.
Job responsibilities
Delivers data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way
Build and optimize batch and streaming data pipelines with strong performance, fault tolerance, and observability
Develop and operate workflow orchestration (e.g., Apache Airflow) to schedule, monitor, and manage data movement and transformations
Model and transform data for analytics using SQL to support business intelligence and reporting workloads
Write production-grade Python/PySpark code with disciplined testing, performance tuning, and maintainable object-oriented design
Collaborate with analysts, data scientists, and application teams to turn requirements into technical designs and delivered solutions
Own critical data systems by improving reliability, scalability, security, and operational excellence
Mentor junior engineers and influence the teams technical direction through standards, reviews, and knowledge sharing
Uses enterprise-authorized AI capabilities within the work environment to accelerate data platform and model design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements
Applies reuse-first, AI-assisted practices within delivery and operational routines (e.g., backup/recovery validation and access control review support), ensuring traceability/auditability and alignment to resiliency and security expectations
Required qualifications, capabilities, and skills
Demonstrated experience delivering in an agile, fast-paced engineering environment. Hands-on professional experience actively coding as a data engineer
Strong software engineering fundamentals (system design, data structures, object-oriented programming, testing strategies, and end-to-end development lifecycle)
Hands-on experience building and operating cloud-based data platforms using major cloud services (e.g., AWS, Google Cloud, or Azure)
Experience with large-scale distributed data processing and performance tuning
Hands-on experience with modern data warehousing/lakehouse technologies. Strong SQL skills and experience with SQL-based transformation tooling
Experience designing and operating orchestration pipelines using Airflow or similar tools
Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity
Ability to review and validate AI-assisted outputs (e.g., model/design summaries or operational checklists) before use, escalating when uncertain and following data handling requirements
