People Data Engineer - 100% Remote Job.

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 employee confidentiality and maintaining appropriate human oversight.
• Own the AI-readiness roadmap for the people data lake end to end — proactively identifying gaps in data quality, architecture, and infrastructure between current state and future AI-ready scale, and driving execution and delivery of the technical solutions that close them.

Technical Thought Leadership and Infrastructure Roadmap

• Serve as the technical authority on the People Data platform’s infrastructure roadmap — bringing forward-looking recommendations grounded in engineering expertise, not just a backlog of requested items.
• Operate as a technical, execution-focused partner to the People Analytics Product Owner (requirements) and the Analytics Products & BI Developer (dashboards) — owning build, delivery, and the technical health of the platform rather than requirements gathering.

Source systems:

• Workday

• 5+ years of progressive experience in data engineering, analytics engineering, data architecture, including experience building production-grade data pipelines, models, integrations, and quality controls.
• Strong HR / People Data experience is a must.
• 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, Azure Cloud (ADF, Synapse, Blob storage, Data Lake etc), version control, automated testing, Databricks.
• Demonstrated ability to proactively identify infrastructure and architecture needs and translate them into a forward-looking technical roadmap, rather than executing solely against a defined backlog.
• Platform preferences: Experience with Workday data, Databricks or a comparable cloud data ecosystem, and BI semantic models.
• Advanced written and spoken English fluency (B2 or C1/C2).

Nice to have:

• Workday, enterprise data warehouses, sensitive-data governance, or AI/ML data enablement is strongly preferred.

Please note that we will not be moving forward with any applicants who do not meet the following mandatory requirements:

• 5+ years of progressive experience in data engineering, analytics engineering, data architecture, including experience building production-grade data pipelines, models, integrations, and quality controls.
• Strong HR / People Data experience is a must.
• Technical capabilities: SQL, Python, data modeling, ETL/ELT, APIs, Azure Cloud (ADF, Synapse, Blob storage, Data Lake etc), version control, automated testing, Databricks.
• Demonstrated ability to proactively identify infrastructure and architecture needs and translate them into a forward-looking technical roadmap, rather than executing solely against a defined backlog.
• Advanced written and spoken English fluency (B2 or C1/C2).

Additional Details:

• Contract Type: Independent contractor (no paid holidays or vacation, tax deductions, or insurance). Compensation is based on gross monthly payment per hours worked.
• Location: 100% remote; client is based in the U.S.
• Contract Duration: 9 months, renewable based on performance (The initial contract runs through 6/30/2027).
• Schedule: Full-time, Monday to Friday, 9 AM to 5 PM EST (full-time, 40 hrs/week)
• Equipment: Contractors must use their own laptop or PC.
• Start Date: As soon as possible.
• Rate: $30 USD/hour.

Next Steps in the Process:

• Introductory interview with Bertoni
• CV review by our partner
• Technical interview with our partner
• Final interview with the end client (possibly a second one)