Data Scientist Lead, Vice President

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

Job summary

JPMorgan Chase & Co. is seeking a Data Engineering Lead to design and evolve a scalable data foundation for trusted analytics and decision-making.

You will deliver resilient datasets, pipelines, and reusable metrics across the PDLC while remaining hands-on when needed. You will drive engineering standards, improve reliability and observability, and mentor a small team to move faster with clear ownership and governance.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 5+ years of hands-on data solutions delivery in a fast-paced engineering environment.
• Strong software engineering fundamentals across design, data structures, testing, and lifecycle.
• Deep understanding of data modeling (dimensional, normalized, event-based).
• Hands-on experience with Databricks and large-scale processing (Spark/PySpark).
• Strong SQL and modern tooling (dbt).
• Experience designing and operating orchestration pipelines with Airflow.
• Proven track record in delivering trusted metrics and maintaining reliability as data sources evolve.
• Ability to lead delivery in complex, multi-stakeholder environments.

Responsibilities

• Design, build, and operate scalable data pipelines with clear SLAs and observability.
• Develop trusted data products with clear ownership and documentation.
• Create well-defined metrics and dataset structures for AI adoption and productivity measures.
• Ensure data quality, governance, lineage, and access controls.
• Develop and manage workflow orchestration (Airflow) for data movement and transformation.
• Model and transform data with SQL/dbt to support reporting and measurement.
• Write production-grade Python/PySpark with testing and performance tuning.
• Collaborate with analytics, product, and engineering stakeholders to align on requirements and success criteria.
• Establish engineering best practices and improve cost/performance through monitoring and runbooks.
• Mentor a team and set technical direction through standards and reviews.

Skills

Data engineering

Spark/PySpark

SQL

dbt

Airflow

Python

Data modeling

ETL pipelines

Observability

Mentoring

Education

Bachelor’s degree in Computer Science/Engineering

Tools

Databricks

Apache Airflow

dbt

Spark

Snowflake

Python

Job description

Job Description

We are seeking aData Engineering Leadto help build and evolve a high-quality measurement data foundation that enables trusted analytics and decision-making at scale. This role focuses on designing and delivering resilient datasets, pipelines, and reusable metrics that support hypothesis-driven analyses and experiments across the product development lifecycle (PDLC).

You’ll be hands-on where needed, drive engineering standards, and help teams move faster by improving reliability, observability, and usability across the data lifecycle—so leaders can clearly see what’s driving value, what’s creating friction, and what operating-model shifts materially improve outcomes as teams become more agentic.

Job Responsibilities

• Design, build, and operate scalabledata pipelines(batch and/or streaming) with clear SLAs, monitoring, and incident response practices.
• Develop and curate trusteddata products(e.g., conformed dimensions, event models, marts) with strong documentation and clear ownership.
• Build and maintainwell-defined metrics and feature-ready datasetsthat enable measurement of AI adoption and productivity outcomes (e.g., reusable aggregates, cohorting, time-windowed measures), including change control as definitions evolve.
• Drivedata quality and governancethrough 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 usingSQL/dbtto support trusted reporting and repeatable measurement.
• Write production-gradePython/PySparkwith 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 spanfinance business cases,PDLC/SDLC tools, andAI 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 of2, influencing technical direction through standards, reviews, and knowledge sharing.

Required 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 withDatabricksand large-scale distributed data processing/performance tuning (Spark/PySpark).
• StrongSQLskills and experience with modern transformation tooling (e.g.,dbt), including building maintainable, testable data codebases.
• Experience designing and operating orchestration pipelines usingAirflow(or equivalent), including backfills, retries, and operational monitoring.
• Demonstrated rigor building and maintaining trustedmetrics(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.

Preferred Qualifications

• Experience with modern lakehouse/warehouse patterns and broader cloud data platforms (e.g., Databricks, Snowflake).
• Experience with BI/semantic layers and metrics management practices.
• Exposure to experimentation or hypothesis-driven analytics approaches (e.g., measurement design to support tests, rollouts, and pre/post evaluation); deep causal specialization not required.