About the job

Technical Responsibilities

• Data Cataloging & Metadata Management: Implement enterprise data catalog and metadata management solutions using Microsoft Purview. Configure Purview to automatically discover data assets, capture data lineage, and manage metadata across on-premises and cloud data sources. This includes setting up business glossaries, data classification schemes, and data lineage tracking so that data is easily discoverable and understood by stakeholders.
• Data Quality Frameworks: Establish and enforce data quality frameworks to continuously monitor and improve the quality of data. Define data quality rules and validation checks (for example, completeness, accuracy, consistency rules) and implement these using Azure data services and Purview’s data quality and scanning features. Set up processes for data profiling and quality exception handling to address data issues proactively.
• Data Governance Tooling: Configure and use Microsoft Purview as a central tool for data governance and data cataloging, ensuring that data assets are properly labeled, classified, and governed in accordance with policies. Leverage Purview’s capabilities to implement data sensitivity labels, retention policies, and access policies as needed. Integrate Purview with Azure Data Factory/Synapse pipelines to ensure lineage and classification information is captured as data flows through the system.
• Privacy and Compliance Enforcement: Implement technical controls to enforce data privacy and protection requirements in all data pipelines and repositories. For example, set up data masking or encryption for sensitive personal data, in compliance with PDPL and related regulations. Ensure that the design of data workflows incorporates privacy-by-design principles and that personal data handling follows the rights and obligations defined by Saudi data protection laws. Continuously monitor data systems for compliance and generate compliance reports or audits as required.
• Data Architecture & Management: Work with data architects and engineers to align data architecture (data lakes, data warehouses, etc.) with governance standards. Ensure that data storage and processing environments in Azure (Data Lake Storage, Azure Synapse Analytics, etc.) are structured to facilitate good governance – e.g. using proper layering, naming conventions, and access controls. Support the development of master data management processes and reference data controls in collaboration with technical teams.
• Troubleshooting and Optimization: Monitor data integration workflows and governance processes for any issues or inefficiencies. Troubleshoot technical problems in data pipelines or catalog scans and optimize performance. Ensure that data governance tools (like Purview) and data integration tools (ADF/Fabric) are kept up-to-date and configured according to best practices. Provide training or handover to client technical teams for ongoing management of these systems.
• Data Integration & ETL Pipelines: Design, build, and maintain robust ETL/ELT data pipelines using Azure Data Factory (ADF) to ingest, transform, and integrate data from various source systems. Ensure pipelines are efficient, scalable, and include error-handling and logging. Where possible, leverage Microsoft Fabric for unified data integration experiences (as it is the next-generation evolution of Azure Data Factory), providing enhanced data workflow capabilities across the analytics ecosystem.

Governance & Strategy

• Implement Data Governance Frameworks: Develop and roll out enterprise data governance frameworks in alignment with the Saudi National Data Management Office (NDMO) standards and guidelines. Ensure these frameworks help clients organize and manage their data effectively as an asset for innovation and compliance.
• Policy and Documentation Development: Design and create comprehensive data governance documentation, including data policies, standards, procedures, and guidelines. These documents will define how data is managed, shared, and protected across the organization, in line with NDMO’s domains (e.g. Data Governance, Data Quality, Data Catalog) and international best practices.
• Operating Model & RACI Design: Develop the data governance operating model for client organizations, clearly defining roles and responsibilities. Establish a RACI matrix (Responsible, Accountable, Consulted, Informed) for data governance roles such as Data Owners, Data Stewards, Data Custodians, etc., to ensure accountability and clarity in data management processes.
• Data Governance Assessments: Conduct data governance and data management maturity assessments using NDMO’s methodology, evaluating both governance practices and technical capabilities. Identify gaps in current state vs. NDMO requirements and recommend remediation steps. This includes assessing areas like data quality, metadata management, privacy, security, and stewardship against the NDMO framework.
• Regulatory Compliance & Advisory: Provide expert guidance to ensure clients’ data practices comply with relevant regulations and laws, including Saudi Arabia’s Personal Data Protection Law (PDPL) and NDMO guidelines. Advise on incorporating data privacy and protection controls into governance policies, aligning with national data regulations that cover data privacy and the protection of personal data. Collaborate with risk and compliance teams to ensure data governance initiatives meet the standards set by SDAIA/NDMO and other regulatory bodies.
• Stakeholder Engagement: Work closely with client stakeholders (executives, business units, and IT teams) to promote data governance awareness and buy-in. Facilitate workshops and meetings to define data strategies, establish data ownership, and communicate the value and progress of data governance initiatives. Adapt governance solutions to different industry contexts (e.g. finance, healthcare, government) as needed for each client.

Qualifications

• Education: Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Management, or a related field.
• Experience: Minimum 5+ years of hands-on experience in data engineering and data governance, with a focus on Azure cloud data environments. Proven track record in implementing data integration pipelines and data governance initiatives at the enterprise level.
• Azure Data Integration: Expertise in Azure Data Factory (ADF) for building complex ETL/ELT pipelines and data workflows. Experience integrating data from diverse sources (both on-premises and cloud) into Azure data lakes or warehouses. Familiarity with Azure Synapse Analytics and Azure Data Lake Storage is a plus for end-to-end data engineering.
• Data Catalog & Governance Tools: Strong experience with Microsoft Purview for data cataloging, metadata management, and data governance solutions. Ability to set up Purview to manage data assets, lineage, and data classification across the enterprise. Exposure to Microsoft Fabric for building unified analytics workflows is highly preferred (demonstrating awareness of the latest Azure data platform developments).
• Data Management Knowledge: Deep understanding of data warehousing concepts and architectures, data lake principles, and enterprise data management best practices. Knowledge of data modeling, SQL database design, and building data lakes/lakehouses. Familiarity with metadata management, data lineage, and master/reference data management practices.
• Governance Frameworks: Good understanding of data governance frameworks and principles (e.g., DAMA DMBOK). Familiarity with NDMO’s data governance and management frameworks – including the Saudi National Data Management and Personal Data Protection Standards – and experience conducting or contributing to data governance assessments or compliance programs in line with NDMO guidance.
• Technical Skills: Strong SQL skills for data querying and manipulation, and proficiency in programming/scripting (Python preferred) for data processing and automation tasks. Experience in using Python for data workflows (e.g., pandas, PySpark) or scripting integration between Azure services is a plus.
• Soft Skills: Excellent communication and presentation skills, with the ability to translate technical concepts into business impacts. Strong stakeholder management and client engagement skills, as this role involves working directly with client teams and executives. Demonstrated ability to lead workshops, gather requirements, and manage project deliverables in a consulting environment.
• Analytical Mindset: Strong problem-solving and analytical thinking capabilities. Attention to detail in assessing data issues and compliance risks. Ability to work independently and collaboratively, managing multiple client projects and deadlines. Familiarity with working in agile project delivery settings is advantageous.

Preferred Certifications

• Microsoft Azure Certifications: Relevant Azure certifications are highly valued, such as Microsoft Certified: Azure Data Engineer Associate (DP-203) or Azure Solutions Architect Expert. These certifications demonstrate expertise in Azure data services and architecture.
• Data Management Certification: Certified Data Management Professional (CDMP) by DAMA International, or an equivalent data governance/management certification, is preferred. This indicates a strong foundation in data governance principles and practices.
• Data Privacy Certification: Certification in data privacy or protection, such as IAPP Certified Information Privacy Manager (CIPM) or equivalent, is a plus. It shows knowledge of personal data protection laws (like PDPL) and the ability to integrate privacy requirements into data governance.