Analytics Engineer

Wellvana; Nashville (TN); On-site; USD 80,000 - 100,000; Full time

Benefits offered by this job

Employee Health Programs

Flexible Work Hours

Career Growth Opportunities

Job summary

Wellvana is seeking an experienced Analytics Engineer to build and scale the data infrastructure for our analytics platform tailored for ACO/MSSP partners. The role is hands-on, involving ownership of dbt models and maintenance of Dagster pipelines to deliver reliable, well-modeled data.

Applicants should have 2-3 years of experience in analytics engineering or related roles, proficiency in SQL and dbt, and a willingness to further learn about healthcare claims data. This position will contribute to a growing team dedicated to transforming the healthcare system.

Qualifications

• 2-3 years of experience as an analytics engineer or data analyst with hands-on SQL and dbt work.
• Healthcare or claims data exposure is a plus, not a requirement.

Responsibilities

• Design and build dbt models on Snowflake.
• Develop and maintain models within a medallion architecture.
• Build and maintain semantic layer definitions for metrics.
• Operate with Dagster pipelines: ingestion, transformation, scheduling.
• Work with CMS claims data for ACO performance reporting.
• Investigate and resolve data anomalies across pipelines.
• Collaborate with analysts to keep data models understandable.

Skills

SQL

Data modeling

dbt

Python

AWS

Healthcare claims data

Education

Bachelor's or Master's degree in computer science, engineering, or related field

Tools

Snowflake

Dagster

Job description

Why Behind Wellvana

The healthcare system isn't designed for health. We're designed to change that. We're Wellvana, and we help doctors deliver life-changing healthcare.

Through our elevated value-based care programs, we're revitalizing an antiquated system that's far too long relied on misaligned incentives that reward quantity of care, not the quality of it.

Our enlightened approach, covering everything from care coordination to clinical documentation education to marketing, ties the healthy outcomes of patients directly to shared savings for primary care providers, health systems, and payors.

Providers in our curated network keep their independence, reduce their administrative headaches, and spend more time with patients. Patients, in turn, get an elevated experience with coordinated care between appointments that is nothing short of life-changing.

Named a 2024 "Best in Business" and 2023 "Best Place to Work" by Nashville Business Journal, we're one of the fastest-growing healthcare companies in America because what we do works. This is the way medicine is meant to be.

Clarity On The Role

Wellvana is seeking an experienced Analytics Engineer to help build and scale the data infrastructure behind Clarity, our analytics platform for ACO/MSSP partners. This is a hands‑on engineering role: you'll own dbt models, build and maintain Dagster pipelines, and work directly with claims and provider data across a growing partner network. You'll work closely with the data engineering team and CTO to turn CMS claims, vendor feeds, and internal source systems into reliable, well‑modeled data that drives partner‑facing analytics and internal decision‑making.

What's Expected

• Design and build dbt models on Snowflake that answer real business and partner questions, not just pass through raw data
• Develop and maintain models within a medallion architecture (bronze/silver/gold), keeping raw, cleaned, and business‑ready layers clearly separated and well‑tested
• Build and maintain semantic layer definitions so metrics are consistent, documented, and reusable across dashboards and partner reporting
• Operate with and extend Dagster pipelines: ingestion, transformation, scheduling, and asset-level data quality checks
• Work with CMS claims data (CCLF, ALR) and provider hierarchy sources (NPI, TIN, CCN, PECOS/NPPES) to support ACO attribution and performance reporting
• Build and maintain vendor data‑sharing pipelines (S3, Iceberg‑format tables, SFTP) for external partners and vendors
• Investigate and resolve data anomalies across ingestion pipelines: row count drops, schema drift, upstream vendor changes
• Diagnose pipeline failures with a bias toward hard failures and root‑cause fixes, not silent fallbacks or defensive workarounds that mask problems
• Collaborate with analysts and other engineers to keep data models understandable and well‑documented
• Increasingly, work alongside AI coding agents (e.g., Claude Code) to accelerate pipeline development, code review, and documentation, and be comfortable with that shifting the shape of day‑to‑day engineering work over time

Requirements

Education

• Bachelor’s or Master’s degree in computer science, engineering, or a related field (equivalent experience considered)

Years Of Related Experience

• 2-3 years of experience as an analytics engineer, data analyst, or similar role with hands‑on SQL and dbt work; healthcare or claims data exposure a plus, not a requirement

Skills / Competencies / Behaviors

• Strong SQL and data modeling skills, comfortable designing dbt models from source data, not just querying existing ones
• Production experience with dbt (models, tests, macros); exposure to an orchestrator (Dagster, Airflow, or similar) a plus
• Familiarity with medallion architecture patterns (bronze/silver/gold) and semantic layer concepts (metrics definitions, reusable business logic)
• Working proficiency in Python for data transformation and light tooling work
• Working knowledge of AWS (S3 basics) and comfort querying cloud data warehouses (Snowflake or similar)
• Interest in healthcare claims data (Medicare claims, CCLF, or similar) a plus; willingness to learn ACO/MSSP attribution logic
• Understanding of data governance and PII/PHI‑aware data handling
• Comfort working with AI coding assistants as part of the standard workflow, not just as a novelty
• Strong troubleshooting instincts and a preference for surfacing problems early over papering over them
• Ability to work independently and communicate clearly with both technical and non‑technical stakeholders