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…