Data Analytics Engineer

Pelago • New York, NY

1d

About The Position

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.

Requirements

• 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

Nice To Haves

• Experience with Looker or similar BI tools and 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 and observability frameworks

Responsibilities

• 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

Benefits

• Generous and meaningful equity package
• Private Health Coverage
• Pension Plan
• Unlimited PTO Policy, Bank Holidays, & company wide “Me Time Days”
• Paid maternity, paternity, & new parent leave
• Flexible working environment
• Annual Learning and Development stipend to support continued learning and career development
• Wellness Reimbursement Program
• Access to Reproductive & Family Planning Care
• Substance Use Support for employees and family members

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed