People Data Engineer

The People Data & AI Readiness Engineer builds and operates the trusted data and AI foundation that powers people analytics, workforce insights, and emerging AI-enabled solutions.

This role translates approved business and product requirements defined by the People Analytics Product Owner, into scalable people-data architecture and production-ready database design, establishes proactive quality and lineage controls, and owns the reliability, governance, and forward-looking infrastructure roadmap of the People Data platform.

Working within the People Analytics team and in close partnership with the People Analytics Product Owner, Enterprise Data Team, HR Technology, Security, and People functional teams, this role executes against prioritized business and product requirements, translating them into secure, governed, and production-ready technical solutions.

This is intended as an execution-focused, technical role rather than a requirements-gathering one. The People Analytics Product Owner defines and prioritizes business requirements, and the Analytics Products & BI Developer builds dashboards; this Engineer translates approved requirements into database design and technical delivery, and independently drives the data lake’s AI-readiness roadmap, data-quality checks, and infrastructure investments based on subject-matter expertise, not solely a backlog of requested items.

In this role, you will lead:

People Data Architecture and Engineering

• Design, build, and maintain scalable people-data models within the enterprise data warehouse — translating requirements from the People Analytics Product Owner into production-ready database design.
• Translate approved business definitions and product requirements into technical data specifications, structures, and reusable data assets.
• Establish architecture patterns that enable consistent use of people data across dashboards, analytics products, scorecards, and approved AI use cases, in close technical partnership with the BI Developer on shared datasets and semantic models.
• Partner with the Enterprise Data Team on source-data onboarding, engineering dependencies, release planning, and production implementation.
• Maintain technical documentation for data models, integrations, transformation logic, dependencies, and platform components.
• Proactively identify and recommend the architecture, platform, and infrastructure investments needed to scale the People Data platform toward a future AI-ready state — grounded in technical expertise rather than a reactive backlog.

Data Quality, Lineage, and Reliability

• Establish automated data-quality monitoring, validation rules, reconciliation controls, and exception alerts for critical people-data elements.
• Implement and maintain end-to-end data lineage, including source, transformation, calculation, and downstream consumption.
• Define and own production support, incident management, and escalation practices for people-data products.
• Monitor platform health, pipeline performance, refresh reliability, and recurring failure patterns — proactively resolving issues before they affect downstream consumers.
• Reduce reliance on manual, person-dependent data checks through repeatable and observable controls.

Data Governance, Privacy, and Access

• Implement technical controls that support approved access, privacy, confidentiality, retention, and sensitive-data handling standards.
• Partner with People Analytics leadership, Privacy, Legal, Security, HR Technology, and the Enterprise Data Team to operationalize people-data governance requirements.
• Contribute technical definitions, source mappings, transformation logic, and lineage information to the people-data dictionary.
• Ensure changes to sensitive people-data structures are appropriately reviewed, tested, documented, and released.

AI Readiness and Enablement

• Build and maintain the governed people-data foundation required for approved AI, machine learning, and advanced analytics use cases.
• Assess whether data proposed for an AI use case is sufficiently documented, accessible, reliable, representative, and appropriately controlled.
• Support the technical design and implementation of the People Data AI Control Tower, including visibility into data readiness, approved use cases, controls, dependencies, and operational health.
• Support design of monitoring and traceability capabilities for approved AI-enabled people-data solutions.
• Partner with governance stakeholders to translate AI standards into enforceable technical controls.
• Enable responsible experimentation while protecting em