[Hiring] Staff Analyst @Fanatics Betting & Gaming

Role Description

You own the data foundation behind how CX Operations understands its performance and how it connects to the broader Fanatics ecosystem. As the Staff Analyst for Ops Ecosystem Analytics, you design and build the architecture, models, and operating standards that make CX Ops ecosystem data in Snowflake trustworthy, scalable, and usable by cross-functional teams, leaders, and by the AI agents we're building on top of it.

This is a hands-on technical role with broad influence. You take raw tables from across the business and turn them into clean, documented, tested models that the rest of the team builds on. You work side by side with Data & Analytics Engineering as well as Strategy teams. You set the SQL and data-modeling bar for the Ops Analytics team without managing anyone. You tell the story of what the data says, regularly presenting to senior leadership and translating architecture decisions and analytical findings into impact they can act on.

We are building toward an AI-first way of operating, and this role is central to it. The right person is technically sharp, relentlessly detail-oriented, and curious about both what the data is saying and how AI changes the way we find and act on answers. You move fast, own ambiguous problems, and hold yourself to a high bar without being told to.

What You'll Do

• Data architecture and operations environment
  • Own the architecture of CX Ops ecosystem data in Snowflake, including schema design, data layers, access patterns, and the operating model that keeps it reliable as it scales.
  • Decide how ecosystem Ops data should be structured, owned, and governed, and drive alignment with necessary teams, like Data Engineering, on those decisions.
  • Consolidate overlapping or one-off datasets into shared, well-governed sources of truth, and set naming and documentation conventions.
  • Define and track data quality standards for Ops ecosystem data.
  • Build alerting on critical metrics as needed so issues are caught before they reach leadership reporting.
• dbt and analytics engineering
  • Turn raw cross-domain tables into clean, tested, reusable dbt models that the whole team can build on.
  • Own the team's core models and introduce data tests, code review practices, and modeling standards that catch issues before they reach stakeholders.
  • Review teammates' SQL, models, and dashboards for correctness, performance, and readability, and coach analysts on best practices.
  • Replace manual, one-off data pulls with automated, always-on solutions.
• Analytical work and insights
  • Lead complex, cross-functional analyses from an ambiguous business problem through scoping, analysis, and a decision-ready recommendation, without formal authority over contributors.
  • Diagnose root causes rather than symptoms, and surface trends, anomalies, and risks without waiting to be asked.
  • Support scenario analysis and impact sizing for product initiatives, operational changes, and staffing decisions.
  • Set the standard for publication-ready dashboards and reporting, with clearly defined KPIs and documented assumptions.
• Communication and influence
  • Present findings, architecture decisions, and impact to Ops and senior leadership. Take a high-uncertainty situation and communicate what you know, what you don't, and what you recommend.
  • Build the narrative around the data so stakeholders understand not just what the numbers say but what to do about them.
  • Mentor and support analysts across the team through standards, reviews, and knowledge sharing.
• AI enablement
  • Make sure agents are built on validated, trusted data foundations, and contribute the data models and evaluation rigor they need before they move to build.
  • Track the performance of deployed agents against Ops goals.
  • Bring a point of view on where AI can be trusted in analytical workflows and where human judgment needs to stay in the loop.

Qualifications

• 5+ years in analytics, analytics engineering, business intelligence, or strategic operations.
• Advanced, hands-on SQL.
• Strong dbt experience.
• Demonstrated experience designing or owning a data environment in a cloud data warehouse (Snowflake preferred), including schema design, governance, and data quality.
• Experience working closely with data or analytics engineering teams to turn raw data into production-ready models.
• Strong executive communication.
• Experience owning complex, cross-functional projects end to end.
• Experience building dashboards in Sigma, Tableau, or a comparable tool.
• High attention to detail.
• Comfortable in fast-paced, ambiguous environments with shifting priorities.

Preferred Qualifications

• Experience supporting AI/ML workflows, agent builds, or automation initiatives.
• Python or a similar scripting language for data manipulation and automation.
• Familiarity with operational KPIs across customer support, fraud, payments, or workforce management.
• Experience in gaming, fintech, or sports, or another operationally intensive, high-volume environment.
• Bachelor's degree in Analytics, Computer Science, Statistics, Economics, or a related field.

Salary Range

$160,000 — $200,000 USD

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