Zus is building a clinical data-first analytics foundation for providers with a focus on value-based care (VBC). Our data powers longitudinal care management across areas like medication adherence, care gap closure, risk stratification, and transitions of care.
We’re hiring an Engineering Manager, to lead this function as a player-coach. This person will grow the team while remaining hands-on in designing and building analytical data products.
This role will shape how analytics engineering enables BI, AI, and agentic systems at Zus by producing high-quality, explainable datasets that accelerate AI-supported healthcare applications.
As part of our team, you will
- Build analytics systems
- Design and build production-grade analytical data models and datasets.
- Translate healthcare data (FHIR, claims, etc.) into reusable analytics assets.
- Develop semantic layers and datasets that power workflows, analytics, and AI/ML applications.
- Perform data engineering tasks (data transformation and workflow orchestration) to reliably land these tables.
- Enable AI-driven data systems
- Create structured datasets that support model training, feature engineering, and feedback loops.
- Partner with AI and Data Science teams to accelerate LLM and agentic AI capabilities.
- Own analytics architecture
- Define the analytics-layer architecture from foundational models to workflow-ready datasets.
Partner with Platform Engineering on ingestion, storage, performance, and scalability.
Implement data quality, governance, and observability. - Lead the team
- Hire, manage and mentor analytics engineers.
- Establish standards for modeling, testing, and documentation.
- Lead through coaching, code reviews, and hands-on technical leadership.
You're a good fit because you...
- Expert SQL and analytical data modeling
- Experience with dbt or similar analytics engineering frameworks
- Experience with modern cloud data warehouses and lakes (Snowflake, BigQuery, SparkSQL)
- Deep automation and data reliability practices (testing, monitoring, CI/CD)
- Understanding of modern BI tools and semantic modeling (Sigma preferred)
- Experience building datasets for machine learning or AI systems
- Experience with data transformation and workflow orchestration tools such as dbt and airflow
- Strong hands-on experience building production analytics systems.
- Experience working with healthcare data such as FHIR, clinical datasets, or claims.
- Experience managing or mentoring analytics/data engineers.
- Ability to balance people leadership with hands-on technical work.
- Interest in building data systems that enable AI-driven healthcare applications.
You'll enjoy our culture if you...
- You enjoy building and leading teams while still remaining hands-on
- You’ve built analytics systems that power products, workflows, or AI systems—not just dashboards
- You’re excited about using LLMs and agentic AI to improve healthcare workflows and analytics systems
- You like designing clean semantic layers and trusted data models that teams can build on
- You enjoy working in early-stage environments where you can shape architecture, standards, and team culture.
- Are in the Boston metro and excited to collaborate in person on a hybrid schedule, or are remote (EST/CST) but willing to travel for in person collaboration occasionally
Top Skills
Zus Health Watertown, Massachusetts, USA Office
101 Walnut St, Watertown, MA, United States, 02472
Similar Jobs
What you need to know about the Boston Tech Scene
Key Facts About Boston Tech
- Number of Tech Workers: 269,000; 9.4% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Thermo Fisher Scientific, Toast, Klaviyo, HubSpot, DraftKings
- Key Industries: Artificial intelligence, biotechnology, robotics, software, aerospace
- Funding Landscape: $15.7 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Summit Partners, Volition Capital, Bain Capital Ventures, MassVentures, Highland Capital Partners
- Research Centers and Universities: MIT, Harvard University, Boston College, Tufts University, Boston University, Northeastern University, Smithsonian Astrophysical Observatory, National Bureau of Economic Research, Broad Institute, Lowell Center for Space Science & Technology, National Emerging Infectious Diseases Laboratories



