Data Governance Engineer

Data Governance Engineer Job Snapshot - Role: Data Governance Engineer - Location: Dubai, United Arab Emirates - Industry: Internet - Function: Information Technology - Experience: Extensive relevant experience; specific years not stated - Job Type: Full Time - Work Arrangement: On-site

talabat is looking for a Data Governance Engineer to establish and strengthen data governance across its growing data ecosystem. The role focuses on ensuring that business data is secure, reliable, discoverable and compliant while making it easier for teams to access and use trusted information. This is a governance ownership role rather than a traditional data pipeline development or platform administration position. You will define data access policies, establish quality standards, evaluate compliance requirements and make independent governance decisions. You will work across Data Engineering, Data Science, Finance, Operations and Product teams to improve data discovery, strengthen access controls and establish consistent business definitions. We are looking for someone who combines practical governance expertise with strong SQL, data modeling and AI capabilities. You should be comfortable using tools such as Claude, semantic layer generators and LLM-based data profilers to automate appropriate governance activities while retaining human judgment for policy decisions, compliance interpretation and escalations. Your goal is to make governance a business enabler: protecting sensitive information without creating unnecessary barriers to responsible data use.

• Country: United Arab Emirates
• City: Dubai
• Industry: Internet
• Function: Information Technology
• Salary: Not disclosed
• Gender: No Preference
• Candidate Nationality: Not specified
• Job Type: Full Time
• Work Arrangement: On-site

• Build and maintain comprehensive data catalogs covering critical organizational data assets.
• Develop machine-readable semantic layers that help employees and AI agents discover and understand trusted data.
• Establish documentation standards for important datasets, business definitions and related metadata.
• Ensure critical data assets have accurate, current and accessible documentation.
• Improve the searchability and discoverability of organizational data.
• Identify gaps in catalog coverage and coordinate improvements with relevant data owners.
• Create a reliable data discovery environment that reduces the time teams spend locating information.
• Take ownership of catalog completeness, documentation quality and practical usability.

• Design and operate Role-Based Access Control (RBAC) frameworks across the organization 's data environment.
• Establish access policies that appropriately segregate information by region, compliance zone and other relevant requirements.
• Review data access requests and identify recurring access patterns.
• Make independent governance decisions on standard access requests.
• Escalate complex policy conflicts or exceptional access situations when necessary.
• Define clear authorization requirements and decision-making procedures.
• Monitor access governance practices to identify and address potential security weaknesses.
• Strengthen preventive controls designed to eliminate unauthorized data access.
• Balance appropriate data protection with efficient access for authorized users.

• Collaborate with Finance, Operations, Product and other domain leaders to define data quality requirements.
• Develop reusable frameworks for evaluating and monitoring dataset quality.
• Establish consistent quality scores covering data freshness, consistency and reliability.
• Define measurable quality criteria for critical business datasets.
• Implement monitoring approaches that identify quality issues and deviations from established standards.
• Work with data owners and technical teams to investigate underlying quality problems.
• Coordinate corrective actions that improve the reliability of business information.
• Ensure critical datasets meet applicable quality requirements.
• Promote consistent data quality practices across business domains.

• Align organizational governance practices with relevant international frameworks, including DAMA/DMBOK and applicable ISO standards.
• Evaluate governance requirements and determine how they should be applied within talabat 's data environment.
• Conduct regular governance maturity assessments covering security, quality and discoverability.
• Identify weaknesses in existing policies, controls and operating procedures.
• Develop practical recommendations for closing governance and compliance gaps.
• Maintain documented and verifiable evidence of compliance with applicable regulatory obligations.
• Establish repeatable compliance review procedures.
• Monitor changes in relevant standards and evaluate their implications for existing governance practices.
• Support continuous improvement in the organization 's data governance maturity.

• Work with business, engineering and data science stakeholders to establish standardized definitions for critical business metrics.
• Align terminology and calculation logic for measures such as active users, revenue and customer segments.
• Identify inconsistencies in existing reporting definitions and data interpretation.
• Embed approved definitions into semantic layers and relevant systems.
• Reduce dependence on manual reconciliation of business metrics.
• Improve consistency between operational reporting, analytics and business decision-making.
• Establish a trusted source of truth for important organizational measures.

• Use AI tools as a regular part of governance analysis, documentation and monitoring workflows.
• Apply Claude and other relevant AI tools to improve governance productivity.
• Use semantic layer generators to support consistent metadata and business definitions.
• Explore LLM-based data profiling for identifying data patterns and quality concerns.
• Automate suitable activities such as documentation generation, pattern detection and quality scoring.
• Evaluate AI-generated findings before using them to support governance decisions.
• Distinguish between activities that can be automated and those requiring direct human judgment.
• Retain responsibility for policy interpretation, sensitive access decisions, compliance assessments and escalations.
• Develop repeatable AI-assisted workflows that reduce manual effort without weakening governance controls.

• Work across business functions, Data Engineering and Data Science to implement governance standards.
• Translate technical governance requirements into clear business language.
• Explain access restrictions, data quality findings and compliance decisions to technical and non-technical stakeholders.
• Build relationships with data owners and domain leaders to establish accountability.
• Coordinate corrective actions when governance controls or quality standards are not met.
• Prepare clear findings documents and recommendations for relevant stakeholders.
• Resolve routine governance questions independently and elevate exceptional cases appropriately.
• Promote an organizational culture in which governance supports responsible data use and business efficiency.

• Extensive practical experience in data governance, data modeling, data quality or compliance.
• Experience working in regulated or rapidly evolving business environments.
• Demonstrated understanding of data governance frameworks and operating practices.
• Experience implementing governance standards or compliance processes across multiple teams.
• Ability to establish policies, evaluate risks and make informed governance decisions.
• Practical understanding of how governance requirements affect business operations and data accessibility.

• Strong working knowledge of Role-Based Access Control frameworks.
• Understanding of data access models, authorization policies and information segregation.
• Experience applying data classification principles and security best practices.
• Ability to evaluate access requests and determine appropriate authorization.
• Understanding of the trade-offs between strict security controls and operational efficiency.
• Confidence in identifying access governance risks and recommending appropriate controls.

• Strong practical proficiency in SQL.
• Solid understanding of data modeling concepts and business data structures.
• Ability to read, evaluate and critique semantic layers.
• Familiarity with LookML and BigQuery.
• Understanding of how business definitions are represented in analytical data systems.
• Ability to identify inconsistencies between data models, business terminology and reporting requirements.
• Technical confidence to collaborate effectively with data engineering and analytics teams.

• Experience defining and evaluating data quality standards.
• Understanding of data freshness, consistency and reliability measures.
• Familiarity with DAMA/DMBOK and relevant ISO governance frameworks.
• Ability to conduct governance maturity assessments and identify improvement opportunities.
• Experience documenting compliance findings and supporting corrective actions.
• Ability to interpret applicable requirements and translate them into practical governance procedures.

• Practical experience incorporating AI tools into everyday professional workflows.
• Familiarity with Claude and AI-assisted semantic layer generation.
• Understanding of LLM-based data profiling and automated documentation approaches.
• Ability to identify governance activities suitable for AI-assisted automation.
• Sound judgment when evaluating AI-generated outputs.
• Clear understanding of when human review and independent policy decisions remain essential.
• Ability to explain which governance activities you perform directly and which you delegate to AI tools.

• Ability to communicate effectively with technical teams, business stakeholders and organizational leaders.
• Strong analytical reasoning and structured problem-solving skills.
• Experience preparing findings documents that communicate risks, evidence and recommendations.
• Confidence in making decisions when governance frameworks are incomplete or conflicting.
• Ability to explain the business implications of technical governance requirements.
• Strong ownership mindset and accountability for governance outcomes.

• Conduct a comprehensive audit of organizational data assets.
• Assess existing data governance maturity against DAMA/DMBOK.
• Define a governance taxonomy with clear severity levels.
• Establish measurable pass/fail criteria for governance assessments.
• Build relationships with business, engineering and data stakeholders.
• Develop a consistent framework for governance decision-making.
• Establish standardized data access policies and review procedures.
• Identify priority risks and improvement opportunities.

• Deploy a data catalog containing documented and searchable critical assets.
• Establish an RBAC framework with automated access review capabilities.
• Introduce data quality frameworks for high-risk business domains.
• Define data leakage standards and relevant preventive controls.
• Establish regular compliance review processes.
• Implement AI-assisted documentation workflows.
• Improve the efficiency of routine governance decisions.
• Significantly reduce access approval times while maintaining appropriate controls.

• Implement risk-tiering logic that classifies data assets as high, medium or low risk.
• Apply governance scrutiny according to the risk associated with each asset.
• Expand automated data quality monitoring across critical datasets.
• Increase catalog coverage and improve asset discoverability.
• Develop a self-service governance library for employees and stakeholders.
• Establish a compliance dashboard for monitoring governance performance.
• Streamline access request processing.
• Help teams view governance as a practical enabler rather than an operational obstacle.

• Maintain a highly complete and searchable data catalog with minimal discovery friction.
• Establish risk-tiered governance as a standard operating model.
• Automate data quality monitoring across critical datasets.
• Ensure most critical data assets consistently meet established quality standards.
• Drive widespread adoption of the self-service governance library.
• Maintain comprehensive documentation supporting compliance audits.
• Make independent governance decisions at scale.
• Use AI-assisted workflows to substantially reduce repetitive manual activities.
• Influence data architecture and design decisions to incorporate governance requirements from the beginning.
• Demonstrate that strong data governance improves business efficiency, trust and responsible data use.

You understand the relationship between governance, engineering and business performance. You can move comfortably between technical discussions about SQL, semantic layers and access controls and business conversations about risk, efficiency and decision-making. You are willing to make informed decisions under ambiguity, even when established frameworks do not provide an immediate answer. You approach governance as a system rather than a collection of disconnected rules. You recognize that effective controls should protect information while helping authorized teams work efficiently. You also understand that governance standards evolve. You can evaluate new requirements, determine their relevance and implement practical changes without introducing unnecessary complexity. Above all, you take ownership of outcomes. You establish standards, verify that they work and coordinate improvements when they do not.

talabat is an on-demand food and non-food delivery platform operating across eight countries in the Middle East and North Africa, processing hundreds of millions of orders annually. The company is part of Delivery Hero, a global online food delivery and quick-commerce group. talabat 's engineering-first culture emphasizes technical quality, accountability and practical problem-solving. As its data ecosystem expands, effective governance plays an important role in maintaining trusted information, protecting sensitive assets and supporting reliable business decisions.