Data Scientist Lead, Vice President

JPMorganChase; Chicago (IL); On-site; USD 133,000 - 175,000; Full time

Job summary

JPMorganChase in Chicago seeks a Data Scientist Lead to guide data pipelines, metrics, and analytics at scale. You will own trusted datasets, implement robust governance, and deliver measurable outcomes across PDLC with a hands‑on approach in Python and PySpark.

The role emphasizes collaboration with analytics, product, and engineering teams, drive for reliability, and leadership of a small team to establish engineering standards and best practices that improve observability and cost efficiency.

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.
• Strong software engineering fundamentals, including design, data structures, and testing strategies.

Responsibilities

• Design, build, and operate scalable data pipelines with clear SLAs and monitoring.
• Develop and curate trusted data products with documentation and ownership.
• Build and maintain metrics and datasets to measure AI adoption and productivity outcomes.
• Drive data quality and governance with validations, lineage, and access controls.
• Develop and operate workflow orchestration (Airflow) to schedule and monitor data movement.
• Model and transform data using SQL/dbt to support trusted reporting.
• Write production‑grade Python/PySpark with testing and performance tuning.
• Partner with stakeholders to define requirements and success criteria.
• Establish and enforce engineering practices and improve observability and cost/performance.
• Mentor a team of 2 and influence technical direction.

Skills

Databricks

SQL

Python

PySpark

dbt

Airflow

Education

Bachelor’s degree in CS/Engineering

Tools

Databricks

Airflow

dbt

Job description

Data Scientist Lead, Vice President

Locations:
1111 Polaris Pkwy, Columbus, OH, 43240, US
8181 Communications Pkwy Bldg F, Plano, TX, 75024, US
880 Powder Mill Rd, Wilmington, DE, 19803, US
10 S Dearborn St, Chicago, IL, 60603, US

Job Schedule: Full time

Base Pay/Salary: Chicago,IL $133,000.00-$175,000.00

Job Information

• Job Identification: 210790084
• Job Category: Data Engineering
• Business Unit: Consumer & Community Banking
• Posting Date: 09/18/2026, 04:59 PM

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.