Head of Data

GS1 Egypt continuously develops its data capabilities to support its products, services and business decisions. We’re looking for a Head of Data to lead data engineering, analytics, data science and data quality, turning business priorities into reliable data services and measurable outcomes.

This role combines strategic direction with delivery accountability. You will lead a multidisciplinary team, establish clear ownership and working practices, and stay close enough to implementation to challenge decisions, resolve obstacles and ensure delivery.

Responsibilities

• Own the data strategy and delivery roadmap, aligning investment and team capacity with business, product and regulatory priorities.

• Lead the Data Architect, data engineering, analytics, data science and data operations and quality functions, with clear responsibilities and performance expectations.

• Translate business needs into prioritized initiatives with defined scope, owners, acceptance criteria and measurable benefits.

• Drive delivery of cloud data platforms, integrations and governed datasets, working with the Data Architect on technical direction and engineers on implementation.

• Establish data governance covering ownership, definitions, cataloging, lineage, access, lifecycle and regulatory requirements.

• Own data-quality improvement across completeness, validity, consistency, duplication and freshness, with automated controls and accountable remediation.

• Establish repeatable methods for data reconciliation, entity matching and regulatory correlation, including validation, exception handling and release approval.

• Partner with Product, Engineering and customer-facing teams to improve data collection and submission controls, respecting source ownership and correction authority.

• Lead internal and customer-facing analytics, enabling self-service BI through reliable datasets, consistent metrics and useful insights.

• Direct AI and machine-learning initiatives toward practical business outcomes, with appropriate evaluation, usage controls and cost management.

• Automate recurring operational work and protect capacity for engineering, analysis and continuous improvement.

• Build team capability through hiring, coaching and performance management, balancing specialist expertise with effective collaboration.

• Manage platform budgets, suppliers and consultancy engagements, retaining internal ownership of knowledge and deliverables.

• Report progress, quality, risks and business impact to executive stakeholders, taking corrective action when commitments slip.

Requirements

• 10+ years of relevant experience across data engineering, analytics or data management, including at least 3 years leading multidisciplinary data teams.

• Proven delivery of data platforms and organizational improvements through implementation, adoption and ongoing operation.

• Strong technical foundation in data engineering, warehousing, modelling, integration and cloud data platforms, sufficient to challenge designs and guide delivery.

• Demonstrated experience establishing data governance and measurable data-quality improvement, including reconciliation, ownership, metadata and lineage.

• Experience managing analytics and BI delivery, translating business questions into trusted metrics, actionable insights and adopted reporting.

• Practical understanding of data science and AI delivery, including data readiness, evaluation, production monitoring and commercial value.

• Experience prioritizing competing operational, product and regulatory demands while maintaining realistic commitments.

• Strong people leadership, including capability assessment, hiring, coaching and performance management.

• Experience managing budgets, vendors and platform costs, with clear accountability for results.

• Ability to communicate data limitations, investment decisions and delivery risks clearly to executive and business stakeholders.

Nice to have

• Experience with regulated product data, reference data, industry standards or cross-organization data exchange.

• Exposure to platforms such as Microsoft Fabric, Azure data services, Databricks or Snowflake, and BI tools such as Power BI, Metabase or Apache Superset.

• Experience developing customer-facing analytics or commercial data services.

• Knowledge of GS1 standards and identifiers.