Sr. Analytics Engineer

Pelago is the leading specialty substance use care provider, built on the belief that effective treatment means matching care intensity to what each member actually needs rather than defaulting to the most expensive intervention. Our programs guide members through every stage of the substance use spectrum, from unhealthy habits to active use disorders, delivering personalized treatment for tobacco, alcohol, opioid, cannabis, and stimulant use based on individual health, habits, genetics, and goals.

With Sona, our voice-first AI Mental Health Specialist, Pelago now applies that same clinically-driven model to mental health, pairing deep clinical expertise with technology to expand access without compromising care quality. We believe technology should make clinical care more precise and more human, not replace the judgment behind it.

Pelago has scaled to helping hundreds of employers and health plans and has already helped more than 750,000 members better manage their substance use. If you're passionate about AI and making an impact on the health of others, join us and make it happen!

Our Data team sits at the center of how Pelago makes decisions. As we scale, we're building a data function that doesn't just report on the business — it shapes it.

We transform complex healthcare and product data into clean, reliable, well-documented models that power reporting, experimentation, AI initiatives, and day-to-day decision-making across the company. The Senior Analytics Engineer is a key force multiplier on that mission.

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.

• 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.

• 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…