Operations Data Analyst

Fanatics Betting & Gaming•Denver, CO

1d•$85,000 - $108,000

About The Position

As an Operations Data Analyst at Fanatics Betting & Gaming, you are on the front lines of how the Operations Department understands its own performance. You sit within the Analytics & AI Enablement team and own the data pipelines, reporting, and analytical work that CX, Fraud, Payments, WFM, and VIP leaders rely on to make decisions every day. This is not a passive reporting role. You are expected to take ownership across multiple operational areas, building and maintaining the data layer, surfacing insights, and flagging risks before they become crises. You work closely within the Strategy & Analytics team to ensure our data is accurate, scalable, and directly connected to Ops top-line goals. We are actively building toward an AI-first way of operating, and this role is part of that. The ideal candidate is technically sharp, relentlessly detail-oriented, and genuinely curious, both about what the data is saying and about how AI can change the way we find and act on answers. You are comfortable moving fast, owning ambiguous problems, and holding yourself to a high bar without being told to.

Requirements

2+ years of experience in an analytical role — business intelligence, data analytics, strategic operations, or a related field.
Strong hands-on SQL experience; ability to write, QA, and optimize complex queries independently.
Experience supporting AI/ML workflows, agent builds, or automation initiatives in an analytical capacity.
Experience building and maintaining dashboards in Sigma, Tableau, or a comparable data visualization tool.
Familiarity with DBT or similar data transformation frameworks.
High attention to detail — you catch data quality issues before they surface in leadership reporting.
Strong communication skills; able to translate analytical findings into plain language for operational stakeholders.
Comfortable operating in fast-paced, ambiguous environments with shifting priorities.

Nice To Haves

Familiarity with operational KPIs across customer support, fraud, payments, or workforce management.
Experience with Python or similar scripting languages for data manipulation and automation.
Experience in gaming, fintech, sports, or other operationally intensive, high-volume environments.
Bachelor's degree in Analytics, Computer Science, Statistics, Economics, or a related field.

Responsibilities

Respond to high-priority analytical requests from Ops leaders — turning raw data into clear, actionable insights on a tight timeline.
Proactively surface trends, anomalies, and risks from the data without waiting to be asked.
Support scenario analysis and impact sizing for product initiatives, operational changes, and staffing decisions.
Own the development and maintenance of dashboards and reports that give operational leaders clear visibility into performance across CX, Fraud, Payments, WFM, and VIP.
Ensure all reporting reflects up-to-date data, clearly defined KPIs, and documented assumptions.
Present findings and data narratives directly to operational stakeholders — translating complexity into clear recommendations they can act on.
Build, maintain, and improve data pipelines that feed Ops reporting and dashboards — ensuring consistent, accurate, and well-documented data flows.
Partner with Data Engineering on DBT development, data store buildout, and pipeline reliability.
Proactively identify and resolve data quality issues; escalate blockers that require cross-functional resolution.
Deprecate manual, one-off data pulls and replace with automated, always-on solutions.
Build alerting infrastructure on critical Ops metrics to catch issues early and reduce reactive firefighting.
Support the AI agent roadmap by contributing data, analytical rigor, and validated data foundations before an agent moves to build.
Track and report on AI agent performance post-deployment — measuring impact against top-line Ops goals.
Actively look for opportunities to apply AI to your own workflow — whether that's speeding up analysis, improving accuracy, or eliminating manual work.
Bring a point of view on where AI can and can't be trusted, and flag where human judgment needs to stay in the loop.