• Own the end-to-end data ecosystem, including source integration, ingestion, orchestration, transformation, storage, modelling, serving and monitoring.
• Design and oversee scalable batch and near-real-time pipelines using tools such as Azure Data Factory, Prefect or Airflow.
• Maintain and evolve cloud and in-house database platforms, particularly Azure, Snowflake, Microsoft SQL Server, MongoDB and SQLite.
• Set up, configure and operate Snowflake environments, including account structure, databases, schemas, virtual warehouses, RBAC, secure data sharing, workload management and cost controls.
• Lead database, data warehouse and third-party platform migrations, including discovery, mapping, reconciliation, cutover, rollback and post-migration assurance.
• Ensure data pipelines are resilient, observable, cost-efficient and supported by appropriate alerting, testing and recovery procedures.
• Define engineering standards for SQL, Python, source control, CI/CD, environments, deployment, secrets, service accounts and technical documentation.

Analytics, Reporting and Business Intelligence

• Own the reporting estate and ensure Power BI, SSRS and QlikView solutions remain accurate, performant, governed and aligned to business definitions.
• Improve semantic models, data marts and self-service capability so teams can access trusted information without creating competing versions of the truth.
• Partner with stakeholders to define KPIs, investigate performance, understand customer behaviour and identify commercial opportunities.
• Guide the transition from reactive reporting towards proactive insight, forecasting, exception monitoring and decision support.
• Maintain oversight of legacy reporting and applications while planning sensible modernisation and retirement pathways.

Data Science, AI and Intelligent Automation

• Identify high-value use cases for machine learning, generative AI, computer vision, natural language processing, optimisation and robotic process automation.
• Lead the design, validation, deployment and monitoring of models such as propensity, customer segmentation, forecasting, similarity and recommendation, pricing, risk, RFV and sentiment analysis.
• Develop and govern LLM-enabled solutions including retrieval-augmented generation (RAG), vector databases, embeddings, document search, assistants, OCR workflows and knowledge tools.
• Ensure prototypes progress into maintainable products with defined ownership, quality controls, security, monitoring, fallback processes and measurable benefit.
• Evaluate internal and third-party AI tools, challenge vendor claims and select the most cost-effective solution for each use case.
• Promote responsible AI practices covering privacy, security, bias, explainability, human oversight and acceptable use.

Governance, Security and Compliance

• Maintain data governance standards covering ownership, lineage, definitions, retention, access, quality, classification and lifecycle management.
• Ensure compliance with UK GDPR, the Data Protection Act and company information-security policies.
• Implement robust role-based access controls, service-account standards, auditability and environment separation across data and AI platforms.
• Work with IT, Security, Legal and business owners to assess risk and ensure new solutions are introduced safely.
• Maintain accurate architecture, process, support and recovery documentation to a consistent professional standard.

Stakeholder and Commercial Management

• Build productive relationships with leaders and subject-matter experts across Stores, Ecommerce, Finance, Marketing, CRM, Merchandising, Operations and Technology.
• Create clear business cases and prioritisation criteria for new initiatives, including effort, cost, risk, dependency, benefit and adoption requirements.
• Own or contribute to budgets, licensing decisions, cloud consumption management and supplier negotiations for the data and AI estate.
• Challenge requirements constructively, simplify solutions where possible and keep delivery focused on outcomes rather than technology for its own sake.
• Present recommendations, progress, risks and results in a concise and credible way to technical and non-technical audiences.

Experience and Qualifications

Essential Experience

• Substantial commercial experience spanning data engineering, data analytics and data science, with evidence of leading a broad data function or major cross-functional programmes.
• Proven experience managing and developing technical professionals, setting standards and delivering through others.
• Strong hands-on capability in SQL and Python, with the judgement to review designs, troubleshoot complex issues and guide engineering decisions.
• Experience designing, operating and improving enterprise data warehouses, lakehouses, data marts, pipelines and reporting platforms.
• Experience delivering database and platform migrations across cloud, on-premise and third-party systems.
• Practical experience with Microsoft Azure and Snowflake, together with strong knowledge of Microsoft SQL Server.
• Demonstrable experience setting up and running Snowflake in a commercial environment, including warehouse sizing, auto-suspend and auto-resume configuration, performance tuning, access-control design, monitoring and spend governance.
• Experience with orchestration tools such as Azure Data Factory, Prefect or Airflow.
• Strong experience administering or governing Power BI, SSRS and QlikView environments.
• Experience delivering machine learning, AI or intelligent automation solutions beyond proof-of-concept stage.
• Hands-on experience designing or delivering RAG solutions, including document ingestion, chunking, embeddings, vector database selection, retrieval quality, prompt orchestration, evaluation and monitoring.
• Strong understanding of data governance, security, privacy, quality and operational support.

Desirable Experience

• Full-stack development experience, particularly where internal tools or data products require an effective user interface or API layer.
• Exposure to MongoDB and SQLite alongside relational database technologies.
• Exposure to VB or VBA, C#, HTML, CSS and JavaScript.
• Experience with GitHub, automated testing, CI/CD, infrastructure management and modern software development practices.
• Retail, ecommerce, CRM, loyalty, customer, pricing, supply chain or finance data experience.
• Experience evaluating and integrating third-party platforms, APIs, SaaS products, RPA tools or AI services.
• A degree or equivalent experience in Computer Science, Data Science, Mathematics, Engineering or a related discipline.

Technical Breadth

• Candidates do not need to have used every technology listed below, but should demonstrate strong architectural judgement, transferable expertise and the ability to learn quickly.
• Cloud and platforms-Microsoft Azure, Snowflake, hybrid on-premise and cloud architecture
• Databases-Microsoft SQL Server, MongoDB, SQLite, relational and non-relational database design
• Engineering-SQL, Python, ETL/ELT, APIs, data modelling, data warehousing, testing and observability
• Orchestration-Azure Data Factory, Prefect, Airflow or comparable workflow platforms
• Business intelligence-Power BI, SSRS, QlikView, semantic modelling and governed self-service reporting
• Development-Git/GitHub, CI/CD, VB/VBA, C#, HTML, CSS, JavaScript and full-stack patterns