People Data Engineer

Summary:

• Work Mode: Hybrid
• ***This is a hybrid role, and candidates should be local to the area or located nearby***

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 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.
• 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.
• 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 production support, incident management, and escalation practices for people-data products.
• Monitor platform health, pipeline performance, refresh reliability, and recurring failure patterns.
• Reduce reliance on manual, person-dependent data checks through repeatable and observable controls.
• 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.

Requirements:

• Minimum of 5 years of progressive experience in data engineering, analytics engineering, data architecture, or a related field, including experience building production-grade data pipelines, models, integrations, and quality controls.
• Experience with HR data, Workday, enterprise data warehouses, sensitive-data governance, or AI/ML data enablement is strongly preferred.
• Technical capabilities: SQL, Python, data modeling, ETL/ELT, APIs, cloud data platforms, version control, automated testing.
• Platform preferences: Experience with Workday data, Databricks or a comparable cloud data ecosystem, and BI semantic models.