People Data & AI Engineer
Indexed description
This is a great opportunity that can provide you with a chance to help Clorox push the boundaries of technology and enjoy the benefits that come from working for Magnit Global. Benefit options available depending on contract factors and upon meeting requirements.
Magnit Global is a leading, global professional services and technology company and a certified “Great Place to Work.” We have been a leader in contingent workforce management since 1991 and work with leading Fortune 500 companies and other large organizations across numerous verticals including consumer electronics, research and development, pharmaceuticals, health services, and many.
The People Data & AI Engineer builds and operates the trusted data and AI foundation that powers people analytics, workforce insights, and emerging AI-enabled solutions. This role designs scalable people-data architecture, integrates data across HR and enterprise platforms, establishes proactive quality and lineage controls, and ensures the reliability and governance of the People Data platform.
Working within the People Analytics team and in close partnership with the Enterprise Data Team, HR Technology, Security, and People functional teams, this role translates prioritized business and product requirements into secure, governed, and production-ready data solutions.
In this role, you will lead
People Data Architecture and Engineering
- 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.
- 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 employee confidentiality and maintaining appropriate human oversight.
- 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.
- Education/equivalent experience Bachelor’s degree in computer science, data engineering, information systems, or a related field—or equivalent practical experience.
- 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.
QUALIFICATION/LICENSURE
Work Authorization Green Card, US Citizen
Preferred Years Of Experience 2 Years
Travel required No travel required
Shift timings
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