About the team

The Data Team builds the foundations and analytical capabilities that support decisions across the business. Our platform includes a data lake, change data capture (CDC), compute clusters, and Apache Airflow, processing tens of terabytes of data with more than 1,000 aggregate runtime hours each day across pipelines and jobs. The team also leads data governance across the business, working with data owners to establish consistent definitions, quality standards, lineage, and appropriate access and lifecycle controls so data is trusted, secure, and used responsibly.

About the role

We’re looking for a Data & Analytics Lead who combines strong technical judgment with a practical understanding of how a business creates value. You’ll turn ambiguous business questions into clear priorities, lead rigorous analysis, and work with stakeholders to turn recommendations into measurable outcomes.

This is a hands-on leadership role. You’ll lead analytics delivery and develop the team while staying close enough to the work to write SQL, review analytical code and data models, investigate discrepancies, and challenge technical decisions. You’ll partner with Data Engineering on reliable, usable data foundations.

Working closely with the Director of Data, you’ll progressively take ownership of planning, stakeholder relationships, team execution, and wider data strategy. The role offers scope to grow into broader leadership of the Data function as you demonstrate readiness and business needs evolve.

What you’ll do

Connect data priorities to business value

Understand the business model, customer journey, operational constraints, and drivers of revenue, margin, cost, retention, and risk; identify where better decisions will create the most value.
Partner with business, Product, Finance, and Engineering leaders to frame problems, challenge assumptions, and agree on the decision, owner, success measure, and action an analysis should support.
Own the analytics roadmap and backlog. Prioritise work by expected impact, effort, urgency, and confidence; negotiate trade-offs and redirect requests that do not justify the investment.
Present clear recommendations, options, financial or operational implications, and uncertainty. Follow through with decision owners to assess adoption and results.

Lead rigorous, hands-on analytics

Lead investigations into performance changes, customer behaviour, growth opportunities, and operational bottlenecks using SQL, Python, and appropriate analytical methods.
Apply segmentation, cohort analysis, funnel analysis, forecasting, and experimentation where suitable. Distinguish correlation from causation and explain the limits of the evidence.
Define consistent KPIs and metric ownership with business stakeholders; build reusable analytical datasets, documented metric definitions, dashboards, and decision tools.
Review queries, analytical code, assumptions, and results for correctness and reproducibility. Trace unexpected numbers to their source and resolve root causes with the relevant owners.

Build trusted, scalable data capabilities

Partner with Data Engineering to translate business needs into requirements for data models, pipelines, instrumentation, freshness, and quality.
Contribute to architecture and tooling decisions, balancing analytical usability, reliability, performance, delivery time, and cloud cost.
Establish practical standards for analytical testing, version control, peer review, documentation, data lineage, and access to sensitive data.
Develop governed self-service analytics, retire redundant reporting, and improve data literacy so teams can answer routine questions confidently.

Lead the team and grow wider ownership

Set clear objectives, assign ownership, manage capacity, and run a predictable delivery cadence while protecting time for foundational improvements.
Coach analysts through reviews, feedback, and development plans; contribute to hiring, performance management, and succession within the team.
Represent Data in planning and performance discussions, resolve competing stakeholder needs, and escalate material risks with clear options.
Work with the Director of Data on function strategy, resource planning, and tooling investment; progressively lead agreed responsibilities independently and document key decisions.

What we’re looking for

Technical and analytical capabilities

7+ years of hands-on experience in analytics, business intelligence, data science, or a related data role, including 2+ years leading a team or substantial cross-functional programmes. Equivalent depth and demonstrated scope are welcome.
Strong hands-on SQL and practical Python skills for analysis, automation, and reproducible workflows.
Sound understanding of statistics, experiment design, measurement bias, and interpreting results under uncertainty.
Experience with analytical data modelling, modern warehouses or data lakes, and BI tools such as Tableau, Power BI, Looker, or equivalent.
Enough depth in pipelines, orchestration, distributed processing, data quality, and cloud cost trade-offs to review proposals and collaborate credibly with engineers.

Business and leadership capabilities

A track record of influencing business decisions with data, with concrete examples of the recommendation, action taken, outcome, and how the impact was assessed.
Strong commercial judgment: able to evaluate opportunity size, unit economics, costs, risks, and the trade-off between analytical precision and timely action.
Experience prioritising a portfolio of requests, aligning senior stakeholders, and constructively challenging assumptions.
Demonstrated ability to coach people, delegate effectively, maintain delivery standards, and build trust across technical and business teams.
Clear written and verbal communication: able to explain a recommendation and its implications to executives, then examine the underlying technical detail with practitioners.

The ownership mindset we value

You stay with a problem until the business question is answered and the next action is clear. You test hypotheses, look beyond the first explanation, surface inconvenient evidence, and acknowledge what remains uncertain. You take responsibility for follow-through and build systems and team capability that continue to work without depending on you for every decision.

Nice to haves

Experience working with Spark, Airflow, CDC, or similarly scaled data platforms.
Experience building an analytics function, introducing governed self-service, or strengthening an analytics engineering practice.
Exposure to budgeting, vendor evaluation, and leading across both analytics and data engineering.
Practical use of AI-assisted analytics or automation, with appropriate validation and protection of company data.