Data Governance Analyst

About the Company

talabat, a leading platform for on-demand delivery of food and non-food items across MENA, operates in eight countries and manages hundreds of millions of orders each year. As part of Delivery Hero, it embraces an engineering-first culture focused on innovation.

Role Overview

As a Data Governance Analyst, you will take ownership of data governance practices throughout the organization. Your focus will be on ensuring data security, quality, accessibility, and regulatory compliance. This position does not involve building data pipelines or managing platforms but centers on defining security and compliance standards, enforcing access policies, rigorously testing adherence, and driving accountability when breaches occur. You will leverage AI technologies such as Claude, semantic layer generators, and large language model-based data profiling tools to scale governance activities while using human judgment for policy and escalation decisions.

Key Responsibilities

• Develop and maintain extensive data catalogs featuring machine-readable semantic layers to enable users and AI agents to locate and understand data effectively.
• Guarantee up-to-date, reliable documentation on all critical data assets and ensure quick searchability.
• Manage discovery processes to provide seamless access to cataloged data assets.
• Design and administer Role-Based Access Control (RBAC) frameworks that segregate data by regions or compliance requirements.
• Independently evaluate and approve common data access requests and escalate complex cases for resolution.
• Prevent unauthorized data access through vigilant monitoring and governance decisions.
• Collaborate with domain experts to create reusable data quality frameworks and monitoring mechanisms.
• Define global quality metrics addressing freshness, consistency, and reliability of data sets.
• Work jointly with teams to address fundamental data quality challenges, ensuring datasets meet required standards.
• Align governance practices with recognized international frameworks such as DAMA/DMBOK and ISO standards.
• Conduct routine maturity assessments on security, quality, and discoverability; partner with stakeholders to bridge identified gaps.
• Maintain thorough, verifiable compliance documentation for all regulatory obligations.
• Work cross-functionally with business, engineering, and data science teams to standardize critical metrics and definitions.
• Integrate consistent definitions into systems to support automated, uniform reporting across the organization.
• Drive the creation of a single source of truth for organizational metrics.

Candidate Profile

Ideal candidates bring substantial experience in data governance, data modeling, data quality, or compliance within dynamic or regulated sectors. Expertise in Role-Based Access Control, access control models, data classification, and security best practices should be demonstrated through practical application rather than just certifications. Fluency in SQL and data modeling is essential, including the ability to understand and assess semantic layers like LookML or BigQuery. You can communicate complex trade-offs between stringent access controls and operational agility to diverse audiences.

Being native in AI usage, especially with tools like Claude and semantic layer generation, is critical. Candidates should know which governance tasks can be automated or augmented through AI—such as auto-documentation, pattern recognition, and quality scoring—and which require nuanced human oversight, including policy setting, compliance interpretation, and escalation management. A clear articulation of your balance between manual and AI-supported workflows is expected.

Development Phases

• Phase 1: Baseline & Foundation — Conduct a thorough audit of all data assets; define a governance taxonomy with precise severity and criteria; assess maturity versus DAMA/DMBOK; establish decision frameworks and data access policies; build cross-functional networks.
• Phase 2: Operationalize & Automate — Deploy data catalogs documenting critical assets; implement RBAC with automated access reviews; create data quality frameworks for high-risk domains; define leakage controls; integrate compliance reviews and AI-assisted documentation; shorten approval processes.
• Phase 3: Scale Through Risk-Tiering — Apply tiered risk logic for asset scrutiny; automate quality monitoring; scale cataloging extensively; build a self-service governance library; create compliance dashboards; streamline access processes; enhance team perception of governance.
• Phase 4: Embedded & Strategic — Achieve highly complete, easily searchable catalogs; mature risk-tiered governance as standard; fully automate quality monitoring; ensure critical data meets standards; utilize self-service governance extensively; document compliance audits; establish autonomous governance decisions at scale; significantly reduce manual work using AI; influence data design from a governance perspective; manage independent policy decisions; position governance as a business enabler.

Additional Information

The role demands fluency in governance, data engineering, and business communication to translate effectively among these domains. Candidates must be decisive under uncertain or conflicting guidelines and possess experience implementing organizational governance standards or compliance processes. Systems thinking and adaptability to evolving governance frameworks are paramount to optimizing governance impact on business velocity.