Data Governance Engineer

Data Governance Engineer

Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets; an ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work Institute and recognized as a 'Cool Vendor' and a 'Vendor to Watch' by Gartner.

Please visit Fractal | Intelligence for Imagination for more information about Fractal.

We are looking for a Data Foundations & Lineage Engineer who will build, document, and maintain the core data ecosystem that powers Learning Data Intelligence. This role will be responsible for defining the structure, lineage, quality, and meaning of datasets in the Learning Lake (e.g., HCM, Finance, HRDP, FDL), and ensuring every dataset is discoverable, well‑documented, and trustworthy.

You will work hands‑on across the Lakehouse, mapping schemas, tracing lineage, profiling quality, eliminating manual dependencies, and building a durable documentation layer that serves engineering, analytics, AI agents, and business stakeholders. This work is foundational to our FY26/FY27 Data Intelligence Strategy pillars of Data Foundation & Technology and Data Quality & Governance.

Data Discovery & Documentation

• Perform deep, brute‑force exploration of all Learning Lake schemas and tables to understand their meaning, business purpose, and dependencies.
• Build a comprehensive documentation repository describing dataset definitions, column‑level semantics, business logic, refresh cadences, source systems, and downstream consumption patterns.
• Translate implicit, tribal‑knowledge data flows into explicit, searchable documentation consistent with guidance

Data Lineage & Architecture Clarity

• Develop end‑to‑end lineage for Learning datasets, mapping sources, transformations, pipelines, and consumption (Power BI, semantic models, AI agents, etc.).
• Identify and eliminate manual or undocumented data feeds, aligning with the Manual Dependency Elimination initiative

Collaboration & Stakeholder Alignment

• Work closely with the DRI team as subject‑matter partners; escalate questions and validate assumptions.
• Partner with analytics, engineering, content, and program teams to ensure data design supports downstream reporting, modeling, and AI use cases.

Enablement & Self‑Service

• Build the foundational metadata that powers data discovery, semantic models, and self‑service analytics.
• Produce guides, readme files, and onboarding materials for all teams relying on Learning Lake.

Required Qualifications

• 4+ years in data engineering, data analysis, data governance, or related fields.
• Strong SQL and data profiling skills; ability to reverse‑engineer unknown datasets.
• Experience with Azure Data Lake, Fabric, Databricks, Synapse.
• Familiarity with metadata systems, data cataloging, and lineage tools.
• Ability to navigate ambiguous, undocumented data environments.

Pay:

The wage range for this role takes into