Senior Analytics Engineer

TGH Senior Center Powered by Greenbrook Medical•New York, NY

1d•$170,000•Hybrid

About The Position

Greenbrook Medical is seeking its first dedicated data professional, a Senior Analytics Engineer, to build upon and enhance its existing data platform. This role involves ensuring consistent performance, expanding the metric library for operator monitoring, and taking end-to-end ownership of the data platform, metric delivery, and dashboard visualization. The position requires building and owning the analytics layer, including dbt models and business logic, as well as the reporting and BI layer used across the business. The Senior Analytics Engineer will also manage production systems transitioned from an engineering partner, ensuring uptime and system integrity, and will serve as the internal counterpart to the engineering team, collaborating with the population health team to define metrics. This is a hybrid role, with a strong preference for New York City, but open to exceptional remote candidates. The role reports to the VP of Growth and Population Health.

Requirements

Fluency in dbt and Python.
Hands-on experience with healthcare datasets and interoperability frameworks (claims, eligibility, EHR data).
Deep understanding of frameworks such as FHIR and HL7.
Solid grasp of business fundamentals to partner with business and clinical managers on metric definition.
Experience owning production systems and responding to incidents.
Bachelor's degree required.
Mindset grounded in core values: Heart, Excellence, Accountability, Resilience, and Teamwork.

Nice To Haves

Desire to deeply own a system, hardening and improving it.
Ability to get to root cause of issues.
AI-native approach, utilizing modern AI tools.
Clear communication skills, able to explain complex data to business users.
Proactive communication.
High bias for action, follow-through on commitments, and closing loops.

Responsibilities

Build and maintain dbt models and business logic for metrics and workflows.
Work within Google Cloud to iterate on business logic and transformation infrastructure.
Ensure consistency in metric definitions across the organization.
Collaborate with performance managers to define measurement needs and success criteria.
Take ownership of production systems transitioned from an engineering partner, ensuring health and integrity.
Be accountable for uptime and system integrity, resolving ingestion and pipeline issues or logic bugs within SLAs.
Develop monitoring and uptime reporting.
Optimize workloads and runtime for system performance.
Continuously improve systems rather than just maintaining them.
Act as the internal counterpart to the engineering partner, resolving issues and escalating as needed.
Build dashboards to support organizational decision-making.
Participate in operating meetings to understand data usage and decision-making processes.