Senior Analytics Engineer

Connie Health Inc•Boston, MA

5h•Hybrid

About The Position

Connie Health is a fast-growing Medicare brokerage on a mission to empower older Americans to make confident, worry-free Medicare plan decisions. We offer a tech-enabled Medicare navigation platform that combines an AI-driven technology with local Medicare experts to help people select optimal healthcare plans and navigate their benefits. Our culture is mission-driven, collaborative, and innovative, as we strive to transform healthcare through data-driven insights and personalized guidance. Our core values are Results-Oriented, Proactive, and Relationships First. Connie Health is seeking an experienced Senior Analytics Engineer to own our data transformation layer end-to-end. You will provide clean, well-modeled, documented datasets that empower the entire company to answer their own questions, applying software engineering practices like version control, continuous integration, testing, and code review to our analytics codebase. Your work spans board-level book-of-business reporting, day-to-day stakeholder self-service through Looker, and AI-driven exploration through tools like Claude. This is a high-leverage role: the quality of our analytics, forecasts, and operating decisions all depend on the foundation you build. You will work closely with our data team and system owners across Engineering, Finance, and Operations to make sure data is trustworthy from source through dashboard. This is a hybrid role, based out of our new Boston office near South Station.

Requirements

Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related field, or equivalent practical experience.
5+ years of professional experience in analytics engineering, data engineering, or a closely related discipline.
Deep, hands-on experience with dbt and strong data modeling practices, including applying software engineering principles to analytics code.
Strong SQL skills and experience with cloud data warehouses (Redshift, Snowflake, BigQuery, or similar).
A track record of normalizing and fixing messy datasets, both within the data layer and by working directly with the owners of the source data (Finance, Engineering, Operations, Marketing, etc.).
A product mindset with the ability to tie technical work to user outcomes and business impact.
Experience enabling self-service analytics for stakeholders through a BI tool, ideally Looker.
Excellent problem-solving skills and the ability to work independently and collaboratively in a fast-paced environment.
Strong communication skills, with the ability to articulate complex technical concepts to both technical and non-technical stakeholders.

Nice To Haves

Experience modeling customer retention and lifecycle metrics across different use cases while ensuring all views tell a consistent story, ideally for a subscription business.
Experience with LookML and Looker administration.
Familiarity with Fivetran, Airflow, or similar data ingestion tooling.
Experience preparing datasets for consumption by AI tools (semantic layers, dataset documentation, definitional consistency).
Prior experience in the healthcare or insurance industry, particularly with Medicare.

Responsibilities

Own the policy dataset and its derived reporting (policy count, retention, effectuation, commission status, cohort analyses). Build the single source of truth that answers the full set of policy-related business questions, with clear naming conventions and documentation that lets stakeholders use it quickly.
Partner with Engineering, Finance, and Operations to improve data quality at the source rather than cleaning it downstream. Catch issues before stakeholders find them in a broken chart.
Build and maintain dbt models using a layered architecture, applying software engineering best practices including version control, continuous integration, automated testing, code review, and documentation. Treat the analytics codebase with the same rigor as production application code.
Configure self-serve Looker models and curate the AI-ready dataset definitions stakeholders consume through Claude, so business users can answer their own questions with reliable, consistent results instead of waiting on a data team ticket.
Engage early in new system implementations to ensure data is captured cleanly from day one and that reporting is ready when the system goes live, rather than retrofitted afterward.
Manage multiple stakeholders and workstreams simultaneously, operating with the independence and judgment expected of a senior IC.