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Lead Data Analyst

exl Service.com (India) Private Limited
CAN
Full-time
Lead · 5–7 years experience
Hybrid
Discovered Today
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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

  • 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.

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