Lead Data Analyst

Lead Data Analyst

Remote; Canada

$90k-130k plus annual bonus

For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits

The posted range is the hiring range for this role - a subset of the broader range available to employees over time - and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.

EXL is looking for a Lead Data Analyst with a passion for sports who will play a key role in providing analytics support to a client within our Sports, Media and Entertainment practice. Based in Toronto, the role sits within the International Fan Data & Analytics team, supporting fanbase growth and engagement across international markets.

The ideal candidate brings 5–7 years of analytics experience, excellent communication and stakeholder management skills, and a strong understanding of the fan lifecycle. You are comfortable working through ambiguity, confident presenting to audiences who are not technical, and motivated by helping the analysts around you do their best work.

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.

Required Qualifications 

• Bachelor’s degree in Analytics, Computer Science, Statistics, Economics, Business or a related field, or equivalent experience.

• 5–7 years of experience in a data analyst or similar role, including experience working directly with business stakeholders.

• Demonstrated ability to present analysis to senior, non-technical audiences and influence decisions.

• Experience mentoring, coaching or reviewing the work of other analysts.

• Strong SQL skills, including complex joins, aggregations and window functions.

• 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.

Responsibilities

Responsibilities:

Identify and shape analytics opportunities with league and club stakeholders 

• Build trusted relationships with club and league stakeholders and understand their strategic priorities, business challenges, and opportunities.

• Proactively identify areas where data and analytics can help clubs grow their fan base, increase revenue, and deepen fan engagement.

• Translate business problems into well-defined analytics opportunities, hypotheses, and use cases

• Help prioritize opportunities based on business impact, data availability, scalability, and feasibility

Translate business needs into analytics solutions 

• Partner with data scientists and analysts to translate business questions into analytical requirements, hypotheses, and solutions.

• Define analytical approaches, success metrics, required data, and expected business outcomes.

• Ensure analytics products and models are designed with the end user and business decision in mind.

Perform hands-on analytics and develop actionable insights 

• Use SQL extensively to extract, transform, analyze, and validate data.

• Perform exploratory and diagnostic analysis to uncover trends, patterns, opportunities, and business drivers.

• Work with large and complex datasets and combine information from multiple data sources.

• Use Python/Pandas and other analytical tools for deeper analysis, modeling, and visualization where appropriate.

• Turn analytical findings into clear, practical recommendations that clubs can act upon

• Maintain technical depth to independently investigate data, validate analyses, and contribute directly to analytical deliverables.

Communicate with stakeholders and support delivery 

• Present complex analytical concepts and findings in a simple, compelling, business-oriented manner.

• Build executive-ready presentations and narratives that connect data to business outcomes.

• Review analytical approaches, outputs, and recommendations to ensure quality and business relevance

Qualifications

Required Skills  

• 5+ years of experience in analytics, business strategy, consulting, or a related field, with meaningful experience working directly with a professional sports club/team or league.

• Demonstrated experience working directly with business stakeholders to identify and solve business problems.

• Strong business acumen with the ability to understand business objectives and translate them into analytics opportunities

• Strong SQL skills and hands-on experience working with large datasets.

• Experience with Python/Pandas or a similar analytics programming language Strong presentation, storytelling, and stakeholder management skills.

• Ability to operate independently in ambiguous environments and proactively identify opportunities.