People Data Engineer

Avispa Technology•Pleasanton, CA

1d•Onsite

About The Position

A leading consumer products company seeks a People Data Engineer to build and operate the data and AI foundation that powers people analytics, workforce insights, and AI-enabled solutions.

Requirements

5+ years of progressive experience in data engineering, analytics engineering, data architecture, or a related field, including building production-grade data pipelines, models, integrations, and quality controls.
Bachelor's degree in computer science, data engineering, information systems, or a related field, or equivalent practical experience.
Technical proficiency in SQL, Python, data modeling, ETL/ELT, APIs, cloud data platforms, version control, and automated testing.

Nice To Haves

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

Responsibilities

Design, build, and maintain scalable people-data models in the enterprise data warehouse, and develop and support data pipelines, APIs, interfaces, and integrations that connect Workday and other HR systems with the enterprise data platform.
Translate approved business definitions and product requirements into technical data specifications, structures, and reusable data assets, establish architecture patterns for consistent use of people data across dashboards, analytics products, scorecards, and approved AI use cases, and maintain technical documentation for data models, integrations, transformation logic, dependencies, and platform components.
Partner with the enterprise data team, HR technology, security, and people functional teams on source-data onboarding, engineering dependencies, release planning, and production implementation.
Establish automated data quality monitoring, validation rules, reconciliation controls, exception alerts, and end-to-end data lineage, define production support, incident management, and escalation practices, and monitor platform health, pipeline performance, refresh reliability, and recurring failures to reduce reliance on manual data checks.
Implement technical controls for data access, privacy, confidentiality, retention, and sensitive-data handling.
Partner with people analytics leadership, Privacy, Legal, Security, and HR Technology to put governance requirements into practice, contribute definitions, source mappings, transformation logic, and lineage to the people-data dictionary, and ensure changes to sensitive data structures are reviewed, tested, documented, and released appropriately.