Data Engineering Lead for Scalable Analytics

JPMorgan Chase & Co.; United States; On-site; USD 140,000 - 190,000; Full time

Job summary

JPMorgan Chase & Co. is seeking a Data Engineering Lead to build a scalable data foundation and enable trusted analytics at scale. You will design resilient datasets, pipelines, and reusable metrics to support hypothesis-driven analyses across the product development lifecycle.

You will be hands-on when needed, setting engineering standards, improving reliability, observability, and usability while mentoring a small team to influence technical direction and best practices.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
• 5+ years of hands-on experience delivering production data solutions in a fast-paced engineering environment (actively coding and owning outcomes).
• Strong software engineering fundamentals (system design, data structures, object-oriented programming, testing strategies, and end-to-end development lifecycle).
• Strong understanding of data modeling (conceptual, logical, physical), including dimensional, normalized, and event-based approaches.
• Hands-on experience with Databricks and large-scale distributed data processing/performance tuning (Spark/PySpark).
• Strong SQL skills and experience with modern transformation tooling (e.g., dbt), including building maintainable, testable data codebases.
• Experience designing and operating orchestration pipelines using Airflow (or equivalent), including backfills, retries, and operational monitoring.
• Demonstrated rigor building and maintaining trusted metrics (definitions, edge cases, validation/testing, documentation) and keeping them reliable as upstream sources change.
• Demonstrated ability to lead delivery in complex environments with multiple stakeholders and ambiguous requirements.

Responsibilities

• Design, build, and operate scalable data pipelines (batch and/or streaming) with clear SLAs, monitoring, and incident response practices.
• Develop and curate trusted data products (e.g., conformed dimensions, event models, marts) with strong documentation and clear ownership.
• Build and maintain well-defined metrics and feature-ready datasets that enable measurement of AI adoption and productivity outcomes (e.g., reusable aggregates, cohorting, time-windowed measures), including change control as definitions evolve.
• Drive data quality and governance through validations, reconciliations, lineage, access controls, retention, and auditability aligned to requirements.
• 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/dbt to support trusted reporting and repeatable measurement.
• Write production-grade Python/PySpark with disciplined testing, performance tuning, and maintainable design.
• Partner with analytics, product, and engineering stakeholders to define requirements, success criteria, and consistent interpretation of key measures—particularly where inputs span finance business cases, PDLC/SDLC tools, and AI tool logs.
• Establish and enforce engineering best practices (version control, code review, testing strategy, deployment processes, runbooks) and continuously improve observability and cost/performance (freshness, completeness, timeliness, scalability, spend).
• Mentor and develop a team of 2, influencing technical direction through standards, reviews, and knowledge sharing.

Skills

Data engineering

SQL

Python

PySpark

Airflow

dbt

Data modeling

Testing

Observability

Mentoring

Education

Bachelor's degree in CS/Engineering

Tools

Databricks

Snowflake

Spark

dbt

Job description

JPMorgan Chase & Co. is seeking a Data Engineering Lead to build a scalable data foundation and enable trusted analytics at scale. You will design resilient datasets, pipelines, and reusable metrics to support hypothesis-driven analyses across the product development lifecycle.

You will be hands-on when needed, setting engineering standards, improving reliability, observability, and usability while mentoring a small team to influence technical direction and best practices.