Overall Purpose We are seeking an experienced and strategic Head of Data Engineering to lead and manage a team of engineering managers across multiple product engineering squads focused on delivering innovative data-centric products. This senior leadership role will be responsible for defining and driving the data platform strategy using Azure Cloud technologies, overseeing the development of data infrastructure, data pipelines, and data products with a strong emphasis on security, scalability, governance, and business impact. The Head of Data Engineering will collaborate with cross-functional teams, align with business goals, and ensure the successful delivery of high-impact data products and systems. Responsibilities: Leadership & Team Management Lead and mentor a team of 6+ Tech Leads overseeing product engineering squads delivering data-centric products. Provide strategic guidance, coaching, and professional development opportunities for engineering managers to empower their teams and deliver results. Establish and maintain a high-performance culture focused on collaboration, innovation, and continuous improvement. Foster a culture of mentorship and leadership development, identifying and nurturing talents Drive the recruitment, retention, and development of top-tier engineering talent within the data engineering team. Drive Data Platform Strategy & Implementation Define and drive the overall data platform strategy with a focus on Azure Cloud to ensure that the organization’s data infrastructure is scalable, reliable, and aligned with business objectives. Oversee the design, implementation, and ongoing optimization of the data platform, leveraging Azure technologies such as Azure Data Factory, Azure Synapse Analytics, Azure Data Lake, and Azure SQL Database. Ensure the data platform supports data engineering, analytics, and data science initiatives across squads. Ensure the data platform enables easy access to high-quality, secure, and compliant data for all stakeholders, fostering a self-service analytics environment. Cross-Squad Coordination & Alignment: Ensure alignment across multiple squads to meet company-wide data objectives and maintain strategic coherence. Facilitate collaboration between product management, data science, analytics, and engineering teams to deliver data-driven products and services. Define shared goals, success metrics, and timelines for squads to ensure that efforts are aligned with broader business goals. Data Strategy & Architecture Develop and execute the data engineering strategy, ensuring alignment with the company’s overall business objectives. Oversee the design and implementation of scalable, reliable, and secure data architectures to support various data products and services. Ensure adherence to best practices for data governance, security, and compliance across the engineering squads. Stay at the forefront of industry trends and emerging technologies, continually improving data engineering capabilities. Metrics-Driven Impact Develop and track success metrics, including data pipeline reliability, availability, and time-to-insight, to evaluate and continuously improve team performance. Communicate the impact of data engineering initiatives through clear metrics to stakeholders at all levels. Operational Excellence & Process Improvement Promote operational excellence through the implementation of efficient data engineering workflows, processes, and tools. Drive the adoption of best practices for data pipeline development, continuous integration/continuous deployment (CI/CD), and data monitoring. Identify and implement opportunities for automation and optimization, improving operational efficiencies across squads. Stakeholder Communication & Reporting Champion the exploration and adoption of new tools, technologies, and frameworks to improve the effectiveness of data engineering processes and product development. Influence the evolution of the company’s data architecture to support emerging needs and business growth, including machine learning and AI-based solutions. Lead efforts to modernize and scale the data infrastructure, ensuring flexibility for future needs. Communicate the status, strategy, and outcomes of data engineering initiatives to senior leadership and other stakeholders. Translate complex technical challenges and opportunities into clear business terms for non-technical audiences. Track and report on key performance indicators (KPIs), providing regular updates on the health and impact of data engineering initiatives. Resource & Project Management Oversee financial planning, budgeting and controling for the data engineering organization (CAPEX, OPEX) Lead the prioritization and allocation of resources across engineering squads, ensuring alignment with business priorities and timely delivery of high-impact projects. Balance short-term needs with long-term strategic goals, ensuring that data engineering efforts are sustainable and scalable. Oversee the management of project timelines, budgets, and deliverables, ensuring successful execution of data product initiatives. Qualifications Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. 10+ years of experience in data engineering, with at least 4-5 years in a leadership or managerial role and a first experience in senior leadership role (manager of managers) Expertise in building and scaling data platforms, with a strong focus on Azure Cloud technologies (e.g., Azure Data Factory, Azure Synapse, Azure Data Lake, Azure SQL Database). Proven experience leading the implementation of data platform strategies and managing large-scale data infrastructure initiatives using Azure. Solid understanding of data architecture, data modeling, and distributed systems. Exceptional leadership skills with the ability to inspire and guide engineering managers and their teams. Excellent communication skills, with the ability to engage with both technical and non- technical stakeholders and align teams around shared goals. Strong experience in Agile methodologies, project management, and resource allocation. Proven success in scaling data engineering teams and processes in fast-growing environments. Knowledge of data privacy, security, and regulatory compliance standards (e.g., GDPR, CCPA). Desirable : Experience with machine learning workflows and AI data product development. Azure certifications (e.g., Azure Data Engineer, Azure Solutions Architect). Experience with DevOps practices and infrastructure-as-code tools. Familiarity with managing remote teams or distributed engineering teams. Benefits and Rewards Hybrid working available, happy to talk flexible working Up to 10% bonus Enhanced holiday scheme (option to buy/sell up to 10 days) and long service awards Critical illness, Life assurance & disability income protection Option to join private medical insurance, subsidised gym membership, and bike to work scheme Contributory pension scheme Wellbeing initiatives and support including Wellbeing App access MyPerks discounts platform. Location : Stratford-Upon-Avon, England, United Kingdom