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Sr. Analytics Engineer

Pelago LogoPelago
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Salary

Base Pay Range $165,000 - $185,000 USD

Location

New York City, NY

Posted

Today

We’re hiring an Analytics Engineer who loves building scalable data models, thinking deeply about business logic, and creating reusable data assets. In this role, you’ll help shape how data is structured and defined across Pelago—ensuring consistency, quality, and usability for everything from dashboards to experimentation to future machine learning use cases. This is a business intelligence role with an analytics engineering toolkit. Reporting to our Director of Analytics, you'll partner with our Commercial, Marketing, and Health Services teams to understand what they need, and then own the dbt and semantic layers that deliver it. We want someone who asks why a number looks the way it does and stays with a problem until it's actually solved.

What you'll do

  • Build and maintain dbt models in Redshift, designed from the business need backward rather than from the raw data forward.
  • Apply software engineering practices to our transformation layer: DRY code, meaningful tests, clear lineage, and documentation.
  • Turn ambiguous questions from our Commercial, Marketing, and Health Services teams into reusable data models and dashboards, built for both analysts and AI.
  • Define and govern consistent metrics through our semantic layer, and keep testing whether they're still the right way to look at the business.
  • Support experimentation and campaign measurement so we know what's working and why.
  • Partner with Data Engineering and Product to understand source data, and with analytics peers to deliver analysis-ready data.
  • Build reusable AI-driven workflows that change how the data team works.
  • Find and fix problems in the data before they become problems in the business.

The background we are looking for

  • 5+ years in analytics engineering, BI, data analytics, or data modeling.
  • Expert SQL and fluency with dbt, Git, and the full analytics development lifecycle, with software engineering discipline: DRY code, meaningful tests, and clear lineage.
  • Strong data modeling and warehousing fundamentals, including clean, well-documented tables with clear grain that work for both analysts and AI.
  • Experience building and governing metric definitions through a semantic layer, in partnership with analysts, data engineers, and product teams.
  • A track record of turning business needs into models and dashboards that stakeholders actually use.
  • Hands-on experience building AI-driven workflows or skills.
  • Comfort operating autonomously while things change.

Nice to have

  • Experience with Cube, Looker, or similar semantic-layer and BI tools.
  • A background in healthcare or other regulated data.
  • Experience measuring marketing campaigns, experiments, or engagement.

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