Head of Data in Manchester

A bit about us:

Established in 1994, The Fragrance Shop is the UK's leading independent fragrance retailer. Our aim is to make mainstream and luxury fragrances affordable and accessible to all. We showcase more than 130 fragrance brands in over 220 stores throughout the UK and online at www.thefragranceshop.co.uk. We are expanding and are looking for a Head of Data to join the team and be part of a growing and vibrant brand.

Why you’ll love working here:

• Enjoy work-life balance with our flexible working scheme - including 15 work from home days a year, duvet days and the choice to flex your working hours.
• Vibrant state-of-the-art office, conveniently located in Trafford Park with great transport links and free onsite parking
• No need to travel to the gym – we have one here for you! Take advantage of our free onsite gym facilities before/after work or even pop in at lunch time
• Generous staff discounts on a wide range of fabulous fragrances
• Excellent progression and development opportunities - work with teams who are passionate about what they do and develop your expertise within a creative and collaborative space

The opportunity

The Fragrance Shop is seeking an experienced and commercially minded Head of Data & AI to lead the next stage of its data, analytics and artificial intelligence journey. This is a broad leadership role combining strategy, people management and hands-on technical oversight across data engineering, business intelligence, data science, automation and AI.

You will own the company data strategy and ensure that trusted, secure and well-engineered data supports decision-making across retail, ecommerce, finance, marketing, CRM, merchandising, operations and the wider business. You will also identify and deliver practical AI and automation opportunities that improve customer experience, colleague productivity and commercial performance.

The successful candidate will be comfortable moving between strategic leadership and technical detail. They will be able to modernise platforms, lead complex migrations, establish strong engineering and governance standards, develop the team and translate emerging technology into measurable business outcomes.

Main purpose of the role

• Set and deliver the data, analytics, data science, automation and AI strategy for The Fragrance Shop.
• Lead, develop and grow the Data team, creating clear ownership, standards, priorities and development pathways.
• Own the reliability, scalability, security and evolution of the company data platform, including Snowflake, and the wider reporting estate.
• Balance business-as-usual delivery, ad hoc insight requirements and strategic transformation programmes.
• Turn business challenges into practical data products, analytical models, automations and AI-enabled services.
• Act as the senior data adviser to the Executive Leadership Team and build strong relationships across all departments.

Key responsibilities

Data strategy and leadership

• Create and maintain a clear multi-year roadmap covering data engineering, analytics, reporting, data science, AI, automation and governance.
• Translate company priorities into an achievable delivery portfolio, with transparent sequencing, dependencies, costs, risks and expected benefits.
• Provide leadership on data architecture, platform selection, build-versus-buy decisions and the responsible adoption of emerging technologies.
• Define measurable service levels and performance indicators for data quality, platform reliability, delivery, adoption and business value.
• Represent Data and AI in senior leadership discussions and communicate complex technical topics clearly to non-technical stakeholders.

People leadership and delivery management

• Lead and develop the existing Data Engineering team and shape the future structure of Analytics, Data Science and AI capability as business needs evolve.
• Set clear objectives, allocate work effectively, coach team members and create progression opportunities across technical and leadership pathways.
• Establish pragmatic delivery practices for planned projects, business-as-usual support, incidents, technical debt and urgent commercial requests.
• Promote a culture of ownership, documentation, peer review, testing, knowledge sharing and continuous improvement.
• Manage specialist suppliers and partners where appropriate, ensuring clear scope, value for money, knowledge transfer and accountable delivery.

Data engineering and platform ownership

• 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 toward 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.

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