Data Governance Analyst

Company Descriptiontalabat 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.

We're part of Delivery Hero, the global leader in online food delivery and q-commerce, and we're engineering-first. Our teams operate underData Governance EngineerAs 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. You know which governance tasks scale through AI (auto-documentation, pattern detection, quality scoring) and which require human judgment (policy decisions, compliance interpretation, escalations). You can describe your workflow specifically: what you do by hand and what you delegate to AI.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
• Ensure every critical data asset has current, trustworthy documentation and is rapidly searchable
• Own the discovery outcome: comprehensively cataloged assets that teams can find without friction
• Security & Access Control:

Design and operate Role-Based Access Control frameworks that keep data appropriately segregated (by region, compliance zone, etc.)Monitor access requests, identify patterns, and make governance calls on standard requests independentlyEscalate complex conflicts but own most access decisions; prevent unauthorized access incidents entirelyData Quality:

• Work with domain leaders (Finance, Operations, Product) to build reusable data quality frameworks and monitoring tools
• Establish global quality scores across freshness, consistency, and reliability
• Partner with teams to resolve underlying quality issues; ensure critical datasets fully meet quality standards
• Compliance & Maturity:

Align practices with international frameworks (DAMA/DMBOK, ISO standards)Perform regular maturity assessments across security, quality, and discoverability; work with teams to close gapsMaintain documented, verifiable compliance across all regulatory obligationsStakeholder Alignment:

Collaborate across business, engineering, and data science to standardize critical definitions (active users, revenue, customer segments)Embed these definitions into systems so reporting consistency is automatic, not manualOwn the outcome: one source of truth for metrics across the organizationWhat 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. You're adaptable: when governance standards evolve, you read them, evaluate applicability, operationalize what matters, and ignore what doesn't.

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; assess maturity against DAMA/DMBOK; build cross-functional relationships; establish decision-making framework and data access policy standard
• Phase 2: Operationalize & Automate
• Deploy data catalog with critical assets documented and searchable; build RBAC framework with automated access review; create data quality frameworks for high-risk domains; define leakage standards; establish compliance reviews; implement AI-assisted documentation; significantly reduce approval time
• Phase 3: Scale Through Risk-Tiering
• Implement risk-tiering logic (high/medium/low-risk assets get proportional scrutiny); deploy automated quality monitoring across critical datasets; scale catalog dramatically; build self-serve governance library; establish compliance dashboard; process access requests efficiently; shift team sentiment from blocker to enabler
• Phase 4: Embedded & Strategic
• Achieve highly complete, searchable catalog with minimal search friction; risk-tiered governance is operating model; quality monitoring fully automated with most critical data meeting standards; self-serve library used comprehensively; compliance audits fully documented; make autonomous governance decisions at scale; use AI-assisted workflows to cut manual work significantly; influence how organization designs data with governance in mind; make independent policy decisions based on data; governance viewed as business enabler, not overhead