Lead Data Engineer: Resilient Data Pipelines & Analytics

J.P. Morgan; New York (NY); On-site; USD 150,000 - 210,000; Full time

Job summary

JPMorganChase in New York seeks a Lead Data Engineer to build and operate resilient, governable data pipelines and architectures across multiple business functions. You will design end-to-end data products, enable lineage and analytics, and partner across cybersecurity, technology controls, engineers, and stakeholders to deliver production-grade solutions.

The role emphasizes strong SQL, Python, data modeling, and cloud/event-driven approaches, with a focus on scalable, auditable data platforms

Qualifications

• 5+ years of relevant experience in data engineering, analytics engineering, or data platform engineering roles, with delivery across the data lifecycle.
• Strong proficiency in SQL, hands-on Python, and data query paradigms including SQL and NoSQL.
• Experience with data modeling, data integration/ETL, and interoperability across multiple systems.
• Experience with database technologies (PostgreSQL, MySQL, MongoDB) including performance optimization.
• Familiarity with big data engines (Apache Spark, Hadoop) and open-source analytics engines.
• Experience implementing data quality, metadata, and governance controls for reliability and auditability.
• Understand modern distributed systems, APIs, and cloud-based/event-driven environments.
• Strong analytical, problem-solving, and collaboration skills.

Responsibilities

• Design, build, and operate production-grade data pipelines ingesting data from disparate sources to deliver trusted data products
• Evolve data models creating comprehensive views of user flows, resiliency signals, and risk measures
• Translate requirements into technical designs and delivery plans across matrix teams
• Implement and improve data quality, metadata, governance, and data lineage across sources and outputs
• Work with microservices, event-driven designs, cloud platforms, and Lambda/Kappa patterns for scalable data needs
• Leverage SQL heavily and apply NoSQL knowledge to optimize databases for performance
• Follow embed automation and engineering best practices (CI/CD, code review, testing, documentation)
• Adopt modern tooling: Python for data engineering, GitHub Copilot and Claude Code subject to firm approvals

Skills

SQL

Python

NoSQL

Data Modeling

ETL

PostgreSQL

MySQL

MongoDB

Apache Spark

Hadoop

Data Governance

Metadata Management

Cloud

APIs

Event Streaming

Distributed Systems

GitHub Copilot

Education

Bachelor's degree in Computer Science, Information Systems, Data Science, or related field

Equivalent practical experience accepted

Tools

PostgreSQL

MySQL

MongoDB

Apache Spark

Hadoop

Job description

JPMorganChase in New York seeks a Lead Data Engineer to build and operate resilient, governable data pipelines and architectures across multiple business functions. You will design end-to-end data products, enable lineage and analytics, and partner across cybersecurity, technology controls, engineers, and stakeholders to deliver production-grade solutions.

The role emphasizes strong SQL, Python, data modeling, and cloud/event-driven approaches, with a focus on scalable, auditable data platforms