Staff Analytics Engineer

Carrum Health•,

2h•$195,000 - $230,000•Remote

About The Position

At Carrum, we live and breathe the mission of transforming the healthcare system to create an unmatched experience for patients. If you are passionate about changing healthcare and want to finally get rid of surprise bills, poor quality, and high prices, while thriving in an entrepreneurial, cutting-edge environment, we would love to connect with you. In 2014 Carrum reinvented the Centers of Excellence (COE) category in digital health. Today, 90% of the US population lives within 50 miles of a Carrum COE and our providers rank in the top 10% nationally. Our team’s execution has been recognized by the venture community and we’ve raised more than $96M in aggregate from investors like OMERS, Tiger Global Management and Wildcat Ventures. Our impact has been externally proven in a 2021 RAND Corporation study [https://www.carrumhealth.com/rand-corp-study-bundled-payments-save/] and featured as a Harvard Business School (HBS) case study [https://www.hbs.edu/faculty/Pages/item.aspx?num=51867]. The salary range for this role is $195,000 - $230,000 depending on geography and level of experience, plus equity and an annual bonus.

Requirements

You have deep analytics engineering roots: 7+ years in analytics engineering or a closely related data role, with a track record of owning architecture, delivery, and long-term maintenance end-to-end.
You're fluent in SQL and Python: You write SQL that's performant at scale and structured for readability, and you use Python to build reusable tooling, automation, and testing frameworks.
You've mastered dbt: You have advanced experience with dbt, including building team practices around testing, documentation, and modularity.
You think in systems, not just queries: You've made data architecture decisions with lasting structural impact, and you can explain the tradeoffs to engineers and business stakeholders in the same conversation.
You elevate the people around you: You've enabled others through mentorship, code review, process, or tooling—not because you have to, but because you want the team to level up.
You've worked at scale in the cloud: You have deep experience with cloud data warehousing (AWS Athena, Snowflake, Databricks, etc.) and cost minimization strategies. Databricks experience is a huge plus, as is experience leading platform migrations.
You care about the semantic layer: You've owned BI tools like Hex as a product, thinking about semantic layer health and cross-team data model standards, not just dashboard enablement.
You thrive in ambiguity: You're comfortable with a startup's pace, proactively seeking out high-value problems before they're on anyone's roadmap rather than waiting to be told what to do.

Nice To Haves

Bonus: you know healthcare: Knowledge of the healthcare tech industry and/or medical/pharmacy claims is a strong plus.

Responsibilities

Shape our data architecture: Design and evolve high-scale dbt models and architecture standards that turn raw data into trusted, well-documented data products across the company.
Build agent-safe infrastructure: Put guardrails, grain definitions, and metadata in place so AI tools and agents can query our data accurately without bypassing governance.
Power self-service in Hex: Build natural-language-driven, self-service analytics experiences in Hex that let anyone at Carrum ask questions of our data directly, without needing to write SQL.
Eliminate friction: Solve recurring pain points—ambiguous metrics, silent pipeline failures, undetected data drift— with reusable frameworks and shared patterns.
Raise the bar on quality: Define what “done” looks like for a data product, building testing, freshness checks, and metadata ownership into how we ship.
Partner across the org: Work closely with Data Engineering, Data Scientists, and business stakeholders to align on use cases and shorten the path from question to trusted answer.
Influence the roadmap: Contribute to roadmap and goal-setting as a decision-maker, communicating tradeoffs clearly to technical and non-technical audiences alike.