Data Analytics and Insights Manager

Title: Data Analytics and Insights Manager Location: Hybrid – Manchester OR Leeds. 3 days on site per week, 2 days remote. Salary: From £60,000 Who we are: interactive investor is an award-winning investment platform that puts its customers in control of their financial future. We’ve been helping investors for nearly 30 years. We’ve seen market highs and lows and been resilient throughout. We’re now the UK’s number one flat-fee investment platform, with assets under administration approaching £75 billion and over 450,000 customers. For a simple, flat monthly fee we provide a secure home for your pensions, ISAs and investments. We offer a wide choice of over 20,000 UK and international investment options, including shares, funds, trusts and ETFs. We also bring impartial, expert content from our award-winning financial journalists, highly engaged community of investors, and daily newsletters and insights. Purpose of the role: As Data Analytics and Insights Manager at interactive investor, you lead our Data Analytics and Insights team and own analysis, reporting, and analytics strategy across the business. You run our centralised centre of excellence for business intelligence, management information, and insight. Your team's work reaches colleagues and decision-makers right across interactive investor. You'll define what the business needs to measure and why, translate that into a prioritised analytics roadmap, and make sure the answers people get are accurate, timely, and genuinely useful. Just as importantly, you'll build the conditions for self-service, so that routine questions can be answered by the people asking them and your team's time goes to the questions that need real thought. You'll set the standards for how analytics is built here: governed metric definitions and a shared semantic layer so numbers agree across the business, a well-maintained data catalog and ontology so data assets are discoverable and trusted, version control, testing, and documentation as normal practice, and AI used as a core, everyday part of the team's workflow, not an optional extra, speeding up everything from exploration to narrative reporting. Working on a "you build it, you run it" basis, your team looks after what it produces - dashboards and reporting products are kept accurate, refreshed as the business changes, and retired when they've served their purpose, so the estate stays trustworthy rather than sprawling. You'll partner closely with peers across the division. The Data Engineering and Platform Manager owns the platform, pipelines, and analytics instrumentation your team depends on; you own what the business needs from it, though in practice the line is deliberately soft, and you'll shape feasibility together from the start. The Data Science and Enterprise AI Manager owns predictive modelling and Enterprise Agents and AI; you'll work together to spot where a model, rather than a report, is the better answer. Beyond the division, you'll partner with teams like Commercial, Product, Marketing, Customer Services, Operations, Compliance, and Legal. As a manager, you'll lead, mentor, and grow a team of Data Analysts, balancing hands-on analysis with strategy and leadership. It's a role that rewards pace and accuracy in equal measure, in a team where people pair on problems rather than pass them over a wall. Requirements Responsibilities: Strategy and technical leadership Define the analytics-as-a-service strategy: what the business needs to measure, why it matters, and how insight reaches decisions Engage stakeholders to understand their questions, translating business need into clear, prioritised analytics briefs Own the analytics roadmap across people, process, and priority, shaped jointly with the Data Engineering and Platform Manager where platform capability is involved Treat AI as an essential, everyday tool across the team's analytics workflows, and continually adopt the capabilities that genuinely speed up or improve the work Work hand-in-hand with the Data Engineering and Platform team (platform, pipelines, instrumentation) and the Data Science and Enterprise AI team (predictive modelling, Enterprise Agents, AI-enabled self-service) to shape feasibility jointly and deliver joined-up outcomes across the Data and Enterprise AI division Serving the business Understand where teams across the business get most value from data, and shape the team's priorities around them Balance and prioritise competing demands transparently, so stakeholders understand what they're getting and when Drive genuine adoption - insight that reaches real decisions rather than stopping at a dashboard Grow self-service, so colleagues can answer routine questions confidently and safely for themselves Partner with the Data Science and Enterprise AI team to deliver AI-enabled self-service analytics, grounded in governed semantic views, so colleagues can query trusted data directly in natural language rather than waiting on bespoke reporting Insight and delivery Build and maintain lightweight data products (e.g. Streamlit apps) for data consumers, as an alternative to traditional BI dashboards where that's the better way to put insight directly in people's hands Deliver analysis and management information that drives commercial performance, product improvement, better customer experiences, and operational efficiency Agree and monitor the KPIs the business runs on, and report clearly on what they're telling us Bring insight into customer behaviour, needs, and journeys to support personalisation, targeting, and product decisions Support experimentation and A/B testing, so changes are measured properly rather than assumed to have worked Standards and quality Own governed metric definitions, a shared semantic layer, the data catalog, and the wider data ontology, ensuring the same question gets the same answer wherever it's asked, data assets are findable and trusted, and metadata, lineage, and business definitions stay accurate as the data estate evolves Set standards for analytics engineering: modelling, version control, testing, documentation, and peer review Monitor and continuously improve data quality with analysts, engineers, and subject matter experts, and be clear about limitations where they exist Keep the reporting estate healthy - refreshed, understood, and retired when no longer needed Governance and compliance Define analytics requirements for data governance and privacy, particularly around web and mobile analytics data Work with Compliance, Information Security, Legal, and Data Engineering and Platform to ensure practice meets the Cookies Policy, GDPR, and other applicable obligations Apply Enterprise AI Centre of Excellence standards where AI-assisted analytics is used, keeping outputs reviewable and appropriately controlled Participate in risk management and root cause analysis, promoting continuous improvement People and delivery Lead, mentor, and develop a team of Data Analysts, managing workload against strategic priority Support hiring, onboarding, and capability development as the team matures Communicate clearly with senior stakeholders, and report honestly on impact, progress, and trade-offs Facilitate knowledge sharing and act as an advocate for good analytics practice across the business SKILLS & EXPERIENCE REQUIRED: Essential Technical and analytical Strong hands-on analytical capability: advanced SQL, data modelling, visualisation, and applied statistics Experience with modern BI and reporting tools, such as Power BI, and lightweight app frameworks such as Streamlit, with a track record of building data products people actually use Experience defining and governing metrics, ideally including semantic layer or single-source-of-truth approaches Solid understanding of digital analytics platforms, such as Google Analytics and Matomo, and of journeys, cookies, and tracking methods - enough to define what's needed, with implementation owned by Data Engineering and Platform Hands-on experience embedding AI as a core, everyday tool in analytics workflows (e.g. AI coding assistants, LLM-based analysis agents), with a clear-eyed view of where it adds most value Experience enabling AI-driven self-service analytics (e.g. natural-language querying grounded in governed semantic views), ideally in partnership with data science or AI teams Experience owning or maintaining a data catalog, metadata, and ontology/taxonomy, alongside metric governance Leadership and delivery Proven experience managing and mentoring a team of data professionals Ability to translate business strategy into an analytics roadmap and prioritise transparently across competing demands Excellent stakeholder management and communication skills across technical and non-technical audiences Governance and domain Working knowledge of data governance and privacy obligations, including GDPR Experience ensuring analytics outputs are accurate, documented, and appropriately controlled Valuable Extras Analytics engineering practice: Dagster, version control, CI/CD, and automated testing Experience with cloud data platforms, such as Snowflake or BigQuery Benefits Group Personal Pension Plan – 8% employer contribution and 4% employee contribution Life Assurance and Group Income Protection Private Medical Insurance – Provided by Bupa 25 Days Annual Leave, plus bank holidays Staff Discounts on our investment products Personal & Well-being Fund – Supporting your physical and mental wellness Retail Discounts – Savings at a wide range of high street and online retailers Voluntary Flexible Benefits – Tailor your benefits to suit your lifestyle Please Note: We will do our utmost efforts to respond to all applicants. However, due to the high volume of applications we're currently receiving, if you haven't been contacted within 30 days of application, please consider unsuccessful. interactive investor operates in accordance with the UK Equality Act 2010. We welcome applications from individuals of all ages, disabilities, gender identities, marital status, pregnancy/maternity, race, religion or belief, sex, and sexual orientation. We are committed to treating all applicants fairly and making reasonable adjustments where needed to support disabled applicants. We actively prevent all forms of discrimination, harassment, and victimisation—whether direct, indirect, associative, or perceptive.