Sr. Analytics Engineer

Location: New York City

Department: Data

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.

What You'll Do:

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

What We Look For:

Required

• 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

Preferred

• 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)