Data Analytics Engineer

New York, NY Pelago is the world’s leading virtual clinic for Substance Use Management. Our program provides guidance, support and treatment for members seeking to overcome their tobacco, alcohol and opioid use. From unhealthy habits to active substance use disorders, Pelago delivers a personalized solution based on individual health, habits, genetics, and goals, providing care for members wherever they might be on the substance use spectrum. Pelago's suite of virtual services ranges from education, to cognitive behavioral therapy (CBT) to comprehensive medication-assisted treatment (MAT). Pelago enables employers and health plans to deliver accessible, affordable, and effective treatment for substance misuse. Pelago has scaled to helping hundreds of employers and health plans and has already helped more than 750,000 members manage their substance use better. We have recently closed our Series C and raised over $151m from leading global investors. If you are passionate about 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 evolving our data function to bridge the gap between raw data and real business insight. We transform complex healthcare and product data into clean, reliable, and well-documented models that power reporting, experimentation, AI initiatives, and day-to-day decision-making across the company. Overview of the Role: We’re looking for 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. What You’ll Do:

Build the foundation for analytics

Design, build, and maintain scalable dbt models for analytics and reporting
Develop modular, well-documented transformation logic in Redshift
Optimize performance and maintainability of transformation pipelines
Implement testing, validation, and observability standards to ensure data quality

Own business logic and metrics

Translate ambiguous business requirements into structured, reusable data models
Define and maintain standardized KPIs and metric logic
Own and evolve Pelago’s semantic layer to ensure consistency across teams
Build reusable data marts for dashboards, experimentation, ROI analysis, and AI workflows
Reduce metric inconsistencies and reporting fragmentation

Partner across the business

Work closely with Data Engineering to ensure reliable upstream pipelines
Collaborate with Product, Clinical, Client Success, Finance, and Growth teams
Support Data Analysts with clean, analysis-ready datasets
Contribute to documentation, data governance, and analytics best practices

Enable advanced analytics

Structure data for experimentation, personalization, ROI measurement, and AI initiatives
Prepare datasets for downstream data science and machine learning workflows
Identify opportunities to improve analytics velocity through better modeling and abstraction

What We Look For:

Required

3+ years of experience in analytics engineering, data analytics or data modeling
Advanced SQL skills and experience building production-grade data models
Strong understanding of data warehousing concepts and dimensional modeling
Experience with dbt and analytics engineering best practices
Experience working in a modern data stack (Redshift Snowflake or similar)
Proven ability to translate business needs into scalable data solutions
Experience working in cross-functional agile environments

Preferred

Experience with Looker or similar BI tools semantic layer design
Familiarity with healthcare data or regulated environments
Experience supporting experimentation ROI measurement or product analytics
Exposure to AI/ML data preparation or feature-ready modeling
Experience implementing data quality testing observability frameworks