Sr. Analytics Engineer

Pelago•New York, NY

7h•$175,000 - $190,000

About The Position

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! About Pelago Data: 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. Overview of the Role: We're looking for a Senior Analytics Engineer who brings deep technical craft and the strategic instincts to match. You don't just build data models — you shape how data is defined, interpreted, and used across Pelago. In this role, you'll drive how data is structured across the company, build the semantic foundations that teams rely on, and bring cross-functional clarity to ambiguous business problems. You operate at the intersection of engineering precision and business judgment, and you're as comfortable influencing stakeholders as writing dbt.

Requirements

5+ years of experience in analytics engineering, data analytics, or data modeling
Expert SQL skills and a track record building production-grade data models at cross-functional scale
Deep understanding of data warehousing, dimensional modeling, and semantic layer design
Advanced dbt skills — you've set patterns, not just followed them
Experience with modern data stacks (Redshift, Snowflake, or similar) and the judgment to make architectural tradeoffs
Demonstrated ability to define metrics and resolve ambiguity across teams — not just implement requirements
Strong stakeholder management and communication skills; you build consensus, not just models
Experience working in cross-functional, fast-moving environments with competing priorities

Nice To Haves

Experience with Looker, Cube.js, or similar semantic layer / BI tooling at an architectural level
Familiarity with healthcare data, regulated environments, or compliance-adjacent data pipelines
Experience supporting experimentation design, A/B analysis, or ROI measurement frameworks
Hands-on experience with AI/ML data preparation, feature engineering, or agentic analytics
Track record of mentoring engineers or raising team-level data quality standards
Experience with data observability tooling (Monte Carlo, Elementary, or similar)

Responsibilities

Lead the analytics foundation
Architect and own scalable dbt model layers that serve analytics, experimentation, AI, and downstream ML workflows
Set the standard for transformation logic, documentation, and observability across the data stack
Drive performance, maintainability, and reliability of Pelago's transformation pipelines
Establish data quality frameworks — testing, validation, monitoring — that the whole team builds on
Own business logic and metrics at scale
Translate ambiguous, cross-functional business requirements into structured, reusable data models — without waiting for full clarity
Define, govern, and evolve Pelago's KPI and metric layer to ensure consistency across teams and tools
Lead development of data marts for dashboards, experimentation, ROI analysis, clinical outcomes, and AI workflows
Proactively identify and resolve metric fragmentation before it becomes a reporting problem
Create scalable solutions beyond resolving known patterns — you find and resolve the ambiguous ones
Drive cross-functional outcomes
Build consensus across Product, Clinical, Finance, Growth, Client Success, and Data Engineering on how shared data assets are defined and used
Navigate competing team priorities and align stakeholders on data modeling decisions
Mentor and support Analytics Engineers and Data Analysts — elevating team output, not just your own
Influence data governance and analytics best practices across the org, not just your squad
Enable advanced and agentic analytics
Structure data assets for experimentation, personalization engines, ROI measurement, and AI/LLM workflows
Identify where AI and automation can change how the data team works — and drive adoption, not just experimentation
Build toward Pelago's Cube/semantic API layer, creating standardized data access for internal and external stakeholders
Partner with Data Science and ML to ensure feature-ready, well-characterized datasets