Head of Data Engineering

We are seeking a visionary, delivery‑focused Head of Data Engineering to lead the transformation of JD’s enterprise data engineering capabilities. Reporting to the Group Director of Data & AI, you will shape a modern, scalable, and secure data estate that accelerates analytics, AI, and digital innovation across the Group. You will lead a Data Engineering function of 15+ within the wider Data & AI organisation, shaping the strategic direction, capability, standards, and operational excellence of data engineering across the group and driving the evolution of our enterprise data engineering capabilities across our cloud and legacy environments. Your remit spans cloud platform engineering, pipeline automation, real‑time data processing, curation, and the enablement of business intelligence, data science and AI teams. You will set the vision for the future of our data infrastructure, ensuring scalability, resilience, security, commercial impact, and alignment to the Group’s strategic objectives. You will manage a highly skilled team of data engineers, while partnering with high‑level stakeholders across technology and business functions to ensure the data foundation unlocks measurable value.

Responsibilities

• Strategic Leadership & Vision
  • Define and own the long‑term data engineering strategy and roadmap driving modernisation, standardisation, and cloud‑first best practices.
  • Lead the migration away from legacy data systems into modern, scalable cloud platforms, ensuring scalability and cost‑optimised use of cloud services.
  • Identify new technologies, patterns, and methodologies (e.g., streaming, ML/AI enablement, data lineage, orchestration) that advance our capabilities.
  • Champion engineering excellence, governance, data quality, and enterprise‑grade reliability across all teams and pipelines.
• Leadership & People Development
  • Lead, mentor, and develop a high‑performing team of data engineering professionals, setting clear expectations and fostering a culture of excellence, collaboration, and continuous improvement.
  • Establish clear role pathways, skills development plans, and a culture of continuous improvement.
  • Drive strong engineering culture, collaboration, and accountability across the team.
  • Manage team capacity, resourcing, and prioritisation to meet business demand effectively.
• Platform & Engineering Delivery
  • Oversee the design, build, and optimisation of cloud‑based data pipelines, datasets, and infrastructure supporting analytics, reporting, and AI products.
  • Ensure data engineering teams deliver reliable and efficient pipelines for ingestion, transformation, enrichment, and curation of large‑scale data.
  • Establish and enforce engineering standards across CI/CD, version control, data modelling, documentation, observability, and code quality.
  • Ensure the creation of trusted, governed data models and consumption layers for downstream stakeholders.
• Stakeholder Collaboration & Influence
  • Build trusted relationships with senior business leaders, acting as a strategic partner to help teams utilise data effectively and make informed decisions.
  • Work with senior business stakeholders to understand priorities, ensuring data engineering directly enables commercial, operational, and strategic outcomes.
  • Partner closely with Data Science, BI, Product, Cloud Infrastructure, Security, and Architecture teams to deliver integrated and scalable solutions.
  • Communicate complex technical concepts clearly and frame engineering decisions in terms of business impact.
• Governance, Security & Compliance
  • Ensure data platforms meet regulatory, security, and compliance requirements, including secure pipeline design, access control, and lineage.
  • Embed good governance practices across metadata, cataloguing, quality, and monitoring.
  • Define and maintain data engineering standards, documentation, and policies to ensure consistency, compliance, and long‑term maintainability.

Role Objectives & KPIs

• Deliver a scalable, modern, and cost‑efficient enterprise data platform.
• Reduce cloud spend and cost‑to‑serve through optimisation and engineering standards.
• Improve the reliability, performance, and availability of mission‑critical data pipelines.
• Increase data quality, consistency, and usability across priority domains.
• Strengthen engineering capability and maturity through talent development and best‑practice standard and frameworks.
• Improved productivity and engagement across Data Engineering teams.
• On‑time delivery of strategic data initiatives in support of analytics, AI, and digital transformation goals.

Skills and Experience

• Significant senior‑level experience in Data Engineering leadership roles (Head, Director, or equivalent) within fast‑paced, complex organisations.
• At least five years’ experience managing and developing high‑performing Data Engineering teams.
• Deep expertise in cloud data engineering within GCP, pipelines, orchestration, and distributed data processing.
• Strong background in SQL, Python, CI/CD, and software engineering best practices.
• Experience designing and scaling data platforms, data models, and ingestion frameworks across structured and unstructured sources.
• Understanding of ML/AI enablement, data curation strategies, and metadata/lineage tooling.
• Proven ability to drive organisational change, modernise technology stacks, and embed best practices.
• Proven ability to coach and mentor technical teams, raising capability and embedding high delivery standards.
• Skilled at simplifying complexity and embedding standards that balance technical rigour with business value and cost.
• Effective communicator who can influence and engage senior stakeholders across business and technology domains who can provide authoritative guidance.
• Experience in large‑scale, multi‑brand, or global enterprises; retail experience is advantageous.
• Demonstrated ability to learn and adopt new technologies, especially emerging AI capabilities.
• Strong leadership and people development skills to develop a growing team of data engineering professionals and foster the adoption of data‑driven decision‑making across the business.