Data Governance Analyst
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Key skills for this role
About the Role
talabat is seeking a Data Governance Analyst to own data governance across the organization, ensuring data is secure, high-quality, discoverable, and compliant. The role involves defining security and compliance policies, managing access control, building data catalogs, and driving data quality frameworks.
Key Skills for This Role
Responsibilities
- Build and maintain comprehensive data catalogs with machine readable semantic layers
- Design and operate Role Based Access Control frameworks to keep data appropriately segregated
- Work with domain leaders to build reusable data quality frameworks and monitoring tools
- Align practices with international frameworks (DAMA/DMBOK, ISO standards) and perform regular maturity assessments
- Collaborate across business, engineering, and data science to standardize critical definitions
Requirements
- Extensive experience in data governance, data modelling, data quality, or compliance in regulated or fast moving environments
- Understanding of RBAC, access control models, data classification, and security best practices
- Fluent in SQL and data modeling; ability to read and critique semantic layers (LookML, BigQuery)
- AI native, using tools like Claude and semantic layer generators daily
Full Job Posting
Company Description
- talabat is the leading on demand food and non food delivery platform in MENA, operating across 8 countries and processing hundreds of millions of orders annually.
- Part of Delivery Hero, the global leader in online food delivery and q commerce.
Role Overview
- As our data estate grows, we need someone to own governance across the organisation—ensuring data is secure, high quality, discoverable, and compliant.
- You're not building pipelines or managing platforms—you're the person who defines what 'secure' and 'compliant' means, makes access policy calls independently, tests for it rigorously, and drives accountability when something breaks.
- You'll use AI tools—Claude, semantic layer generators, LLM based data profilers—as your default to scale governance work while knowing when to stay human for policy decisions and escalations.
Your Profile
- You have extensive experience in data governance, data modelling, data quality, or compliance in regulated or fast moving environments.
- You understand RBAC, access control models, data classification, and security best practices as lived experience, not certifications.
- You're genuinely fluent in SQL and data modeling; you can read and critique semantic layers (LookML, BigQuery).
- You can articulate trade offs between strict access control and enabling velocity, and write findings documents that land with both technical and non technical audiences.
- You're AI native—using Claude and semantic layer generators as part of your daily flow.
What You'll Own
- Data Discovery: Build and maintain comprehensive data catalogs with machine readable semantic layers that enable both humans and AI agents to understand and find accurate information.
- Security & Access Control: Design and operate Role Based Access Control frameworks that keep data appropriately segregated (by region, compliance zone, etc.).
- Data Quality: Work with domain leaders (Finance, Operations, Product) to build reusable data quality frameworks and monitoring tools.
- Compliance & Maturity: Align practices with international frameworks (DAMA/DMBOK, ISO standards).
- Stakeholder Alignment: Collaborate across business, engineering, and data science to standardize critical definitions (active users, revenue, customer segments).
What Sets You Apart
- You speak multiple languages fluently: governance, data engineering, and business—constantly translating between them.
- You're comfortable making calls under ambiguity; when frameworks conflict or are incomplete, you decide what to do and own the outcome.
- You've shipped governance standards or compliance processes across teams.
- You think in systems—seeing how governance changes enable business velocity—and you optimize for it.
How You'll Progress
- Phase 1: Baseline & Foundation — Complete comprehensive audit of all data assets; define governance taxonomy with specific severity levels and pass/fail criteria.
- Phase 2: Operationalize & Automate — Deploy data catalog with critical assets documented and searchable; build RBAC framework with automated access review.
- Phase 3: Scale Through Risk Tiering — Implement risk tiering logic (high/medium/low risk assets get proportional scrutiny); deploy automated quality monitoring.
- Phase 4: Embedded & Strategic — Achieve highly complete, searchable catalog with minimal search friction; risk tiered governance is operating model.
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