Data Scientist Lead, Vice President

JPMorgan Chase; Kentucky; On-site; USD 120,000 - 195,000; Full time

Job summary

JPMorgan Chase is seeking a Data Engineering Lead to build a measurement data foundation, delivering scalable pipelines and reusable metrics to support hypothesis-driven analytics across the PDLC. You will be hands-on when needed and set engineering standards for reliability and observability.

Collaborate with analytics, product, and engineering partners to define requirements, ensure data quality, implement Airflow-based orchestration, and mentor a small team while improving cost efficiency and

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 with SLAs and incident response practices.
• Develop and curate trusted data products with clear ownership.
• Build and maintain well-defined metrics and feature-ready datasets for AI adoption and productivity measures.
• Drive data quality and governance through validations, lineage, access controls, retention, and auditability.
• Develop and operate workflow orchestration (Airflow) to schedule, monitor, and manage data movement and transformations.
• Model and transform data for analytics using SQL/dbt to support trusted reporting.
• Write production-grade Python/PySpark with disciplined testing, performance tuning, and maintainable design.
• Partner with analytics, product, and engineering stakeholders to define requirements and success criteria.
• Establish and enforce engineering best practices and continuously improve observability and cost/performance.
• Mentor and develop a team of 2, influencing technical direction through standards, reviews, and knowledge sharing.

Skills

Python

PySpark

SQL

Data modeling

Airflow

dbt

Big data architectures

Code testing

Mentoring

Performance tuning

Education

Bachelor's degree in Computer Science or Engineering

Tools

Databricks

Spark

Apache Airflow

dbt

Job description

We are seeking a Data Engineering Lead to 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 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.

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 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.

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.