People Data Engineer & AI Engineer

Overview Client•Pleasanton, CA

1d•Hybrid

About The Position

Client is seeking a People Data & AI Engineer to build and operate the trusted data and AI foundation that powers people analytics, workforce insights, and AI-enabled solutions. This role is responsible for designing scalable people-data architecture, integrating data across HR and enterprise platforms, and ensuring the reliability, governance, and quality of the People Data platform. Working within the People Analytics team and in partnership with Enterprise Data, HR Technology, Security, Legal, Privacy, and People functional teams, this position translates business and product requirements into secure, governed, and production-ready data solutions.

Requirements

Minimum 5 years of progressive experience in data engineering, analytics engineering, data architecture, or a related field, including experience building production-grade data pipelines, data models, integrations, and quality controls.
Strong technical experience with SQL, Python, data modeling, ETL/ELT processes, APIs, cloud data platforms, version control, and automated testing.

Nice To Haves

Experience working with HR data, Workday, enterprise data warehouses, sensitive-data governance, or AI/ML data enablement.
Experience with Workday data, Databricks or a comparable cloud data ecosystem, and BI semantic models.

Responsibilities

Design, build, and maintain scalable people-data models within the enterprise data warehouse.
Develop and support data pipelines, APIs, interfaces, and integrations connecting Workday and other People systems with the enterprise data platform.
Translate approved business definitions and product requirements into technical specifications, reusable data assets, and data structures.
Establish architecture patterns that enable consistent use of people data across analytics products, dashboards, scorecards, and approved AI use cases.
Partner with enterprise stakeholders on source-data onboarding, engineering dependencies, release planning, and production implementation.
Maintain technical documentation for data models, integrations, transformation logic, dependencies, and platform components.
Establish automated data quality monitoring, validation rules, reconciliation controls, and exception alerting for critical people-data elements.
Implement and maintain end-to-end data lineage, including source systems, transformations, calculations, and downstream consumption.
Define production support, incident management, and escalation processes for people-data products.
Monitor platform health, pipeline performance, data refresh reliability, and recurring failure patterns.
Reduce reliance on manual data validation through repeatable, automated, and observable controls.
Implement technical controls supporting approved access, privacy, confidentiality, retention, and sensitive-data handling standards.
Partner with Privacy, Legal, Security, HR Technology, and enterprise data teams to operationalize people-data governance requirements.
Contribute technical definitions, source mappings, transformation logic, and lineage documentation to the people-data dictionary.
Ensure sensitive people-data changes are appropriately reviewed, tested, documented, and released.