Lead Data Analyst

Responsibilities 

Stakeholder Partnership & Communication 

• Act as the main analytics point of contact for marketing, international and regional stakeholders, building trusted working relationships across the client organization. 

• Translate ambiguous business questions into clearly scoped analysis with agreed objectives, success measures and timelines. 

• Present findings and recommendations to senior stakeholders, tailoring the level of detail to the audience and leading the discussion that follows. 

• Write clear summaries and narratives that make complex analysis easy to act on, and challenge or reframe requests where the data points to a better question. 

• Manage expectations on scope, priorities and delivery dates, and keep stakeholders informed as work progresses. 

Team Leadership & Mentorship 

• Serve as the senior analyst on the team: set analytical standards, review other analysts’ work and give constructive, practical feedback. 

• Mentor and coach junior and mid-level analysts on technical skills, business context and stakeholder communication. 

• Prioritize and coordinate incoming analytics requests across the team, allocating work and unblocking colleagues. 

• Build and maintain documentation, reusable code and shared definitions so the team’s work is consistent and repeatable. 

Hands-on Analysis 

• Write efficient SQL and Python to extract, clean, transform and analyze large fan and marketing datasets. 

• Work within cloud data platforms (Databricks, Snowflake, AWS) to build and maintain analytical datasets and workflows. 

• Develop audience segments and test/control designs that support 1:1 marketing campaigns and deepen fan engagement. 

• Summarize large data outputs into clear, business-friendly takeaways. 

Reporting, Advanced Analytics & Data Quality 

• Own the recurring reporting on fan growth, engagement and lifecycle metrics across international markets. 

• Monitor campaign and go-to-market (GTM) performance against agreed KPIs and success criteria, and recommend where to optimize. 

• Develop and maintain advanced analytics models, such as segmentation, propensity, churn and customer lifetime value, and translate their output into recommendations stakeholders can act on. 

• Build and maintain forecasts of fan growth, engagement and campaign performance to support planning and target setting across international markets. 

• Measure and validate model and forecast accuracy over time, and refine the approach as markets and data evolve. 

• Support user acceptance testing (UAT) of database changes and help data engineering teams with root cause analysis (RCA) of data issues. 

• Work with data engineering on business requirements, and liaise with third-party partners on data needs and definitions. 

• Proficiency in Python for data analysis (e.g., pandas, NumPy). 

• Hands-on experience with at least one cloud data platform: Databricks, AWS or Snowflake. 

• Experience building advanced analytics models and forecasts (e.g., segmentation, propensity, churn, time series) and applying them to business decisions. 

• Strong analytical and problem-solving skills, with close attention to detail. 

Preferred Qualifications 

• Experience with BI and visualization tools (e.g., Tableau, Power BI, Looker). 

• Experience in marketing, CRM or customer analytics. 

• Knowledge of consent management, identity resolution or privacy regulations such as PIPEDA and GDPR. 

• Experience in a consulting or client-facing environment. 

• Passion for sport and an understanding of global fan cultures.