Data Scientist Lead, Vice President

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, normaliz...