Senior Data Analyst, Spotlight Sports Group

• Advanced SQL capability working with large-scale data warehouses (Snowflake experience desirable)
• Strong Python skills for analysis and automation (pandas, numpy; modelling libraries advantageous)
• Experience working with behavioural event data (Segment, Amplitude, GA, Mixpanel or similar)
• Proven experience influencing business decisions through analysis
• Experience mentoring or managing analysts
• Comfortable operating in ambiguous problem spaces
• Strong communication skills with the ability to challenge constructively
• Demonstrated commercial awareness

Desirable

• The Senior Analyst sits within the Data Intelligence Unit (DIU) and is responsible for the operational leadership of the analytics function
• This role combines hands-on analytical expertise with people leadership and delivery ownership
• Own the day-to-day running of the analyst team
• Line manage and develop analysts
• Control workload prioritisation and intake
• Maintain analytical quality and standards
• Deliver high-impact commercial insight
• This is not a pure individual contributor role
• It is a player–coach position responsible for building capability and improving output quality across the function
• The role reports into the Head of Data / DIU and acts as the operational bridge between strategic direction and delivery execution
• Own the intake, scoping and prioritisation of analytics requests
• Manage workload planning and sequencing across the team
• Ensure deadlines and delivery standards are met
• Act as first point of escalation for analytical queries
• Line manage Analysts and Associate Analysts
• Conduct 1:1s, development planning and performance management
• Identify capability gaps and support hiring decisions
• Foster a culture of analytical rigour and commercial curiosity
• Lead complex analytical initiatives across Content, CRM, Product and Commercial
• Move beyond reporting to uncover behavioural drivers and quantify impact
• Shape hypotheses with stakeholders and translate findings into actionable recommendations
• Develop and oversee robust test-and-learn frameworks
• Review analytical outputs for methodological accuracy and clarity
• Promote best practice in SQL, Python and experimentation design
• Improve documentation and reproducibility standards
• Champion data governance and responsible data usage
• Identify opportunities to streamline recurring analysis
• Improve workflow efficiency and reduce manual reporting
• Support scalable analytics infrastructure