Data Scientist Lead, Vice President

Next Frontier Capital; Kentucky; On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Health care

On-site health centers

Retirement plan

Backup childcare

Tuition reimbursement

Mental health support

Financial coaching

Job summary

Next Frontier Capital seeks a Data Engineering Lead to build a high-quality measurement data foundation enabling trusted analytics at scale. You will design resilient datasets, pipelines, and reusable metrics across the PDLC, mentoring a small team and raising engineering standards.

You will work hands-on with SQL/dbt, Databricks, and PySpark, driving observability, data quality, and cost/performance. Collaboration with analytics, product, and engineering is essential for cross-functional

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
• 5+ years delivering production data solutions in a fast-paced engineering environment.
• Strong software engineering fundamentals (system design, data structures, OOP, testing strategies).
• Strong understanding of data modeling (conceptual, logical, physical).
• Hands-on experience with Databricks and large-scale distributed data processing/PySpark.
• Strong SQL skills and experience with modern transformation tooling (dbt).
• Experience designing and operating orchestration pipelines using Airflow (backfills, retries, monitoring).
• Demonstrated rigor building and maintaining trusted metrics and keeping them reliable as sources change.
• Ability to lead delivery in complex environments with multiple stakeholders and ambiguous requirements.

Responsibilities

• Design, build, and operate scalable data pipelines with SLAs and monitoring.
• Develop and curate trusted data products with clear ownership and documentation.
• Build and maintain metrics and datasets to measure AI adoption and productivity.
• Drive data quality and governance through validations, lineage, access controls, retention.
• Develop and operate Airflow workflows to schedule and monitor data movement.
• Model and transform data for analytics using SQL/dbt to support trusted reporting.
• Write production-grade Python/PySpark with testing and performance tuning.
• Partner with analytics, product, and engineering to define requirements and success criteria.
• Establish engineering best practices and continuously improve observability and cost.

Skills

Hands-on data engineering

SQL / dbt

Databricks

PySpark

Airflow

Data modeling

System design

Testing strategies

Leadership

Mentoring

Education

Bachelor’s degree in CS/Engineering

Tools

Airflow

dbt

Databricks

Spark/PySpark

Job description

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.

Chase is a leading financial services firm, helping nearly half of America’s households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

Equal Opportunity Employer/Disability/Veterans

Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We’re proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction. Data Scientist Lead to help build and evolve a high-quality measurement data foundation that enables trusted analytics and decision-making at scale.