Lead Data Engineer

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 140,000 - 220,000; Full time

Job summary

JPMorganChase is seeking a Lead Data Engineer in the Corporate Sector to advance data collection, storage, access, and analytics platforms with secure, scalable designs.

You will own and optimize batch/streaming pipelines, develop Airflow-based workflows, and transform data for BI workloads using SQL and Python/PySpark. Collaboration with analysts and data scientists is essential.

Qualifications

• Formal training or certification on data engineering concepts and 5+ years applied experience

Responsibilities

• Delivers data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way

Skills

Python

PySpark

SQL

Data modeling

Cloud platforms

Agile

Software engineering

ETL

Education

Data engineering training or certification

Tools

Airflow

AWS

GCP

Azure

Data warehousing

Job description

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 firm’s 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 team’s 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

• Formal training or certification on data engineering concepts and 5+ years applied experience
• 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)
• Strong understanding of creating and maintaining data models (conceptual, logical, and physical), including dimensional and normalized modeling approaches
• 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