SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Pelago Health is a leading specialty substance use care provider that matches treatment intensity to individual member needs. The company has scaled to serve hundreds of employers and health plans, helping over 750,000 members manage substance use disorders. Pelago recently launched Sona, a voice-first AI Mental Health Specialist, combining clinical expertise with technology to expand access to care.
The Data team sits at the center of Pelago's decision-making infrastructure. As the company scales, they're building a data function that shapes business strategy, not just reports on it.
As Senior Analytics Engineer, you'll be a key force multiplier on this mission. You'll architect scalable dbt model layers serving analytics, experimentation, AI, and ML workflows. You'll set standards for transformation logic, documentation, and observability across the data stack, establishing data quality frameworks that the entire team builds upon.
You'll translate ambiguous cross-functional business requirements into structured, reusable data models. You'll define, govern, and evolve Pelago's KPI and metric layer to ensure consistency across teams and tools. You'll lead development of data marts for dashboards, experimentation, ROI analysis, clinical outcomes, and AI workflows, proactively identifying and resolving metric fragmentation.
Cross-functional collaboration is central to the role. You'll build consensus across Product, Clinical, Finance, Growth, Client Success, and Data Engineering on how shared data assets are defined and used. You'll mentor Analytics Engineers and Data Analysts, elevating team output. You'll influence data governance and analytics best practices across the organization.
You'll structure data assets for experimentation, personalization engines, ROI measurement, and AI/LLM workflows. You'll identify where AI and automation can change how the data team operates and drive adoption. You'll partner with Data Science and ML to ensure feature-ready, well-characterized datasets and build toward Pelago's semantic API layer.
