Head of Engineering

How will I make a difference?

The Head of Data Engineering will 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.

Key Responsibilities:

1. Leadership & Team Management:

o Lead and mentor a team of 6+ Tech Leads overseeing product engineering squads delivering data-centric products.

o Provide strategic guidance, coaching, and professional development opportunities for engineering managers to empower their teams and deliver results.

o Establish and maintain a high-performance culture focused on collaboration, innovation, and continuous improvement.

o Foster a culture of mentorship and leadership development, identifying and nurturing talents o Drive the recruitment, retention, and development of top-tier engineering talent within the data engineering team.

2. Drive Data Platform Strategy & Implementation:

o 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.

o 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.

o Ensure the data platform supports data engineering, analytics, and data science initiatives across squads.

o Ensure the data platform enables easy access to high-quality, secure, and compliant data for all stakeholders, fostering a self-service analytics environment.

3. Cross-Squad Coordination & Alignment:

o Ensure alignment across multiple squads to meet company-wide data objectives and maintain strategic coherence.

o Facilitate collaboration between product management, data science, analytics, and engineering teams to deliver data-driven products and services.

o Define shared goals, success metrics, and timelines for squads to ensure that efforts are aligned with broader business goals.

4. Data Strategy & Architecture:

o Develop and execute the data engineering strategy, ensuring alignment with the company’s overall business objectives.

o Oversee the design and implementation of scalable, reliable, and secure data architectures to support various data products and services.

o Ensure adherence to best practices for data governance, security, and compliance across the engineering squads.

o Stay at the forefront of industry trends and emerging technologies, continually improving data engineering capabilities.

5. Metrics-Driven Impact:

o Develop and track success metrics, including data pipeline reliability, availability, and time-to-insight, to evaluate and continuously improve team performance.

o Communicate the impact of data engineering initiatives through clear metrics to stakeholders at all levels.

6. Operational Excellence & Process Improvement:

o Promote operational excellence through the implementation of efficient data engineering workflows, processes, and tools.

o Drive the adoption of best practices for data pipeline development, continuous integration/continuous deployment (CI/CD), and data monitoring.

o Identify and implement opportunities for automation and optimization, improving operational efficiencies across squads.

7. Innovation & Technology Leadership:

o Champion the exploration and adoption of new tools, technologies, and frameworks to improve the effectiveness of data engineering processes and product development.

o Influence the evolution of the company’s data architecture to support emerging needs and business growth, including machine learning and AI-based solutions.

o Lead efforts to modernize and scale the data infrastructure, ensuring flexibility for future needs.

8. Stakeholder Communication & Reporting:

o Communicate the status, strategy, and outcomes of data engineering initiatives to senior leadership and other stakeholders.

o Translate complex technical challenges and opportunities into clear business terms for non-technical audiences. o Track and report on key performance indicators (KPIs), providing regular updates on the health and impact of data engineering initiatives.

9. Resource & Project Management:

o Oversee financial planning, budgeting and controling for the data engineering organization (CAPEX, OPEX)

o Lead the prioritization and allocation of resources across engineering squads, ensuring alignment with business priorities and timely delivery of high-impact projects.

o Balance short-term needs with long-term strategic goals, ensuring that data engineering efforts are sustainable and scalable.

o Oversee the management of project timelines, budgets, and deliverables, ensuring successful execution of data product initiatives.

Benefits & Rewards

• Dual Location / Hybrid worker Status
• Participation in our annual Incentive Plan (VIP) - up to 10% bonus
• 25 days annual leave plus bank holidays
• Option to buy and sell up to 10 days annual leave
• Access to voluntary benefits including private medical insurance, cycle to work scheme, subsidised gym membership
• Automatic inclusion in Life Assurance, Critical Illness and Disability Income protection schemes
• Pension scheme up to 8% employer contribution
• Access to reward & discount platform
• Wellbeing initiatives
• Volunteering day

We reserve the right to bring forward the closing date of our job vacancies if we receive a suitable number of high-quality applications from which to make a shortlist. We recommend that you apply for our roles as soon as possible rather than wait until the published closing date