Data Governance Analyst

Company Overview

talabat is the premier on-demand platform for food and non-food deliveries across the MENA region, operating in eight countries and handling hundreds of millions of orders each year. As a subsidiary of Delivery Hero, a global leader in online food delivery and quick-commerce, talabat emphasizes strong engineering practices.

Role Summary

As our data environment expands, we require a dedicated Data Governance Analyst to lead governance initiatives ensuring our data remains secure, compliant, easily discoverable, and of top quality. This position focuses on defining and enforcing data access policies rather than building pipelines or managing platforms. The role involves independently making access control decisions, rigorously testing compliance, and holding teams accountable for governance breaches. The analyst will leverage advanced AI tools like Claude, semantic layer generators, and LLM-based data profilers to enhance governance efficiency while discerning when human judgment is essential for policy decisions and escalations.

Key Responsibilities

• Develop and maintain comprehensive, machine-readable data catalogs with semantic layers to facilitate easy and accurate discovery for both users and AI agents.
• Ensure all critical data assets are well-documented, trustworthy, and quickly searchable.
• Design and manage Role-Based Access Control (RBAC) systems that enforce regional and compliance-based data segregation.
• Handle standard data access requests autonomously, identifying patterns and making governance decisions independently; escalate only complex issues.
• Prevent unauthorized access incidents by vigilant monitoring and governance enforcement.
• Collaborate with leaders in Finance, Operations, and Product to create reusable frameworks and monitoring tools for data quality, including establishing global scores for freshness, consistency, and reliability.
• Proactively resolve data quality issues, ensuring critical datasets meet defined quality thresholds.
• Align governance practices with international standards such as DAMA/DMBOK and ISO regulations, performing ongoing maturity assessments and closing identified gaps.
• Maintain comprehensive, verifiable compliance documentation across all regulatory requirements.
• Work jointly with business, engineering, and data science teams to unify critical definitions (e.g., active users, revenue, customer segments) and embed these into systems to guarantee consistent reporting.
• Serve as a liaison fluent in governance, data engineering, and business languages to navigate ambiguity, make autonomous decisions, and champion governance that facilitates rather than hinders business velocity.

Qualifications & Profile

• Substantial experience in data governance, data modeling, data quality management, or compliance within regulated or fast-paced environments.
• Deep understanding of Role-Based Access Control (RBAC), data classification, and security best practices derived from practical application rather than certifications.
• Proficient in SQL and data modeling; capable of critically analyzing semantic layers such as LookML and BigQuery.
• Able to communicate complex trade-offs between strict access controls and operational agility to both technical and non-technical stakeholders.
• Experienced in employing AI tools (Claude, semantic layer generators) as part of daily workflows with clear distinctions on tasks suited for automation versus those requiring human oversight.
• Exceptional ability to translate among governance, technical, and business domains, with a strong orientation toward systems thinking and scalable governance solutions.
• Adaptable to evolving governance standards by assessing applicability, operationalizing relevant measures, and disregarding inapplicable ones.

Career Progression

• Phase 1 - Baseline & Foundation: Conduct full audits of data assets, define taxonomy and evaluation criteria, assess maturity per DAMA/DMBOK, establish decision frameworks and data access policies.
• Phase 2 - Operationalize & Automate: Launch a data catalog with documented key assets, implement RBAC with automated reviews, develop data quality frameworks, define data leakage standards, deploy AI-supported documentation, and reduce approval cycle times.
• Phase 3 - Scale Through Risk-Tiering: Apply risk-tiered scrutiny on data assets, automate quality monitoring on critical datasets, expand catalog coverage, create a self-service governance library, and improve compliance dashboards and request processing efficiency.
• Phase 4 - Embedded & Strategic: Attain a nearly complete, readily searchable catalog, fully operational risk-tiered governance model, automated quality monitoring meeting standards, comprehensive self-service library utilization, fully documented audits, autonomous governance decision-making, AI-enhanced workflows minimizing manual effort, proactive influence on data design for governance, and recognition of governance as a business enabler.