Staff Data Analyst
New
F
Fanatics Betting & GamingSports betting
Location: United States; Workplace Type: RemoteFull-TimeStaff
Salary160,000 - 200,000 USD per year
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Job Details
- Experience
- 5+ years in analytics, analytics engineering, business intelligence, or strategic operations.
- Required Skills
- PythonSQLSnowflakeTableauData modelingdbt
Requirements
- Have 5+ years of experience in analytics, analytics engineering, business intelligence, or strategic operations.
- Demonstrate advanced, hands-on SQL skills, including writing, QA, and optimization of complex queries.
- Have strong dbt experience designing model layers, writing tests, and making architectural decisions.
- Have experience designing or owning a cloud data warehouse environment, including schema design, governance, and data quality; Snowflake is preferred.
- Have experience working with data or analytics engineering teams to turn raw data into production-ready models.
- Have presented analysis and recommendations to senior leaders and can defend methodology under scrutiny.
- Have owned complex cross-functional projects end to end.
- Have experience building dashboards in Sigma, Tableau, or a comparable tool.
- Preferred: experience supporting AI/ML workflows, agent builds, or automation initiatives.
- Preferred: Python or a similar scripting language for data manipulation and automation.
- Preferred: familiarity with operational KPIs across customer support, fraud, payments, or workforce management.
- Preferred: experience in gaming, fintech, sports, or another high-volume operational environment.
Responsibilities
- Own the architecture of CX Operations ecosystem data in Snowflake, including schema design, data layers, access patterns, and operating standards.
- Align with Data Engineering and other teams on how Ops data is structured, owned, and governed.
- Consolidate datasets into shared sources of truth and establish naming, documentation, and data quality standards.
- Build clean, tested, reusable dbt models from raw cross-domain tables.
- Establish data tests, code review practices, and modeling standards; review SQL, models, and dashboards for correctness, performance, and readability.
- Replace manual data pulls with automated solutions and build alerts for critical metrics as needed.
- Lead complex cross-functional analyses and deliver decision-ready recommendations, scenario analyses, and impact sizing.
- Present findings and architecture decisions to Ops and senior leadership, and mentor analysts through reviews and knowledge sharing.
- Provide validated data foundations for AI agents and track their performance against Ops goals.
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