About this role

Boulder Care is seeking a skilled and experienced Senior Analytics Engineer to support both data infrastructure and business insights, with a likely focus on growth, marketing, and patient acquisition analytics. The ideal candidate will operate across the entire data pipeline, collaborating closely with our Principal Data Engineer to build reliable, scalable data assets while empowering analysts and stakeholders to make data-driven decisions.

This role is key to ensuring that clinical, operational, and growth data is modeled securely and remains reliable and accessible. The Senior Analytics Engineer must also be skilled at translating complex business questions into robust analytical workflows, with particular emphasis on helping teams understand and optimize the patient acquisition funnel, marketing performance and attribution, conversion, and growth opportunities.

About Boulder

Boulder Care is an award-winning digital clinic for addiction medicine, recognized for both innovation and high-quality of patient care. Founded in 2017 by CEO Stephanie Strong, our mission is to improve the lives of people with substance use disorders through compassionate, evidence-based care.

We provide Boulder patients with a fully virtual, multidisciplinary care team—including medical providers and peer recovery specialists—who deliver personalized treatment, including medication for opioid use disorder (MOUD) and ongoing support. Our approach is grounded in clinical excellence, patient-centered care, and a commitment to reducing barriers to recovery. Boulder partners with leading health plans, employers, and community organizations to ensure that our services are accessible and covered for the people who need them most.

Named by Fortune as one of the Best Workplaces in Healthcare, we foster a culture of kindness, respect, and meaningful work that delivers outstanding patient outcomes and moves the addiction medicine industry forward.

Key responsibilities

Data Modeling & Pipeline Development

Operate across the data stack to build, manage, and optimize end-to-end data pipelines from ingestion to reporting.

Develop, test, and maintain robust, production-grade data models and schemas in BigQuery using dbt, ensuring clean lineage and optimized performance.

Collaborate with the Principal Data Engineer to implement and refine efficient ETL/ELT workflows, balancing infrastructure scalability with analytical agility.

Configure and manage data ingestion pipelines using Fivetran to integrate new operational, financial, and clinical data sources.

Stakeholder Partnership & Analytical Translation

Serve as a primary partner to clinical, operations, finance, and growth teams, translating ambiguous business requests into structured, actionable data projects.

Confidently and clearly communicate data insights, technical constraints, and data definitions to both technical and non-technical leaders.

Design and maintain semantic layers and scalable reporting structures within Looker (LookML) to enable self-service analytics across the organization.

Team Mentorship & Capability Building

Act as a technical mentor and escalation point for data analysts, building their capabilities across SQL, dbt, and advanced analytical workflows.

Establish best practices for code reviews, version control, and testing within the analytics team to foster a high-performance engineering culture.

Bridge the gap between raw data engineering and downstream analysis, ensuring analysts have the well-structured datasets they need to succeed.

Data Quality, Governance & Documentation

Implement robust data validation, testing (e.g., dbt tests), and monitoring to ensure the highest integrity and completeness of upstream and downstream data.

Collaborate with data leadership to define and enforce data standards, metrics definitions, and single-source-of-truth architectures.

Maintain comprehensive documentation of data models, definitions, and governance policies to ensure institutional knowledge is transparent and accessible.