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Analytics Engineering Manager

Underdog LogoUnderdog
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Salary

$215,000 - $240,000

Location

United States/Remote

At Underdog, data isn’t just an asset — it’s a competitive advantage. As our first Analytics Engineering Manager, you’ll lead the function that turns raw data into the foundation for insights, metrics, and decision-making. This is a unique opportunity to shape a nascent team, establish best practices, and build the semantic layer and models that power analytics across the business. You’ll be both a technical contributor and a leader, ensuring that Underdog’s data is reliable, consistent, and a true driver of growth.

About the role

  • Define and execute the Analytics Engineering team charter and roadmap, clarifying scope, ownership, and priorities.
  • Lead, mentor, and grow a team of Analytics Engineers, fostering a culture of technical excellence and business impact.
  • Personally contribute (as a “player-coach”) to high-impact dbt models and pipelines, setting the standard for code quality and best practices.
  • Stand up and operationalize a semantic layer (dbt MetricFlow, Omni, or equivalent) as the single source of truth for company metrics.
  • Implement engineering rigor in analytics workflows: version control, CI/CD, testing frameworks, and observability.
  • Partner with Data Engineering, Analytics, and Data Science to ensure seamless handoffs from ingestion to analysis.
  • Deliver early business-critical wins (e.g., Finance-ready revenue model, Marketing attribution pipeline) that demonstrate AE’s impact.
  • Build trust and alignment across stakeholders with clear communication, documentation, and recurring forums.

Who you are

  • Experienced data leader with 7+ years in analytics engineering, BI, or data engineering, including 2+ years mentoring or managing teams.
  • SQL and dbt expert, fluent in modern data modeling patterns and cloud warehouses (Snowflake, BigQuery, Databricks).
  • Skilled in workflow orchestration (Airflow, Dagster, Prefect), Git-based workflows, and CI/CD in data.
  • Strong understanding of semantic layers and their role in enabling consistent, scalable self-service analytics.
  • Effective communicator who builds trust across technical and business stakeholders.
  • Bias for action — thrives in a fast-paced startup environment and enjoys solving ambiguous problems.

Even better if you have

  • Experience building or scaling an Analytics Engineering team from scratch.
  • Exposure to sports, fantasy sports, or gaming data.
  • Familiarity with real-time or streaming architectures.
  • Hands-on experience with data observability tools (Monte Carlo, Elementary).
  • Contributions to the analytics engineering community (open-source, blogs, talks).

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