Automation and Reporting Analyst II

Collective Health•Lehi, UT

12d•Hybrid

About The Position

At Collective Health, we’re transforming how employers and their people engage with their health benefits by seamlessly integrating cutting-edge technology, compassionate service, and world-class user experience design. As a Health Care Data Analyst, you will serve as a bridge between complex healthcare data and scalable analytical solutions. You will be responsible for delivering high-impact insights by developing production-ready code and automating data pipelines. You won’t just run reports; you will partner with engineering and product teams to build self-service capabilities via Looker, manage codebase integrity, and ensure our analytics workflows are modular and efficient. This role is ideal for a technical analyst who views "data as a product" and is passionate about improving healthcare through data excellence.

Requirements

Experience: At least 2 years of experience in healthcare analytics, with a focus on claims data, or member experience.
Python Competency: Proficiency in Python for data manipulation (e.g., Pandas, NumPy) and automation scripts. Ability to write reusable functions and modular scripts.
Git Proficiency: Demonstrated experience using Git/GitHub for version control, including branching, merging, and resolving conflicts within a team setting.
Technical Proficiency: Advanced SQL optimization and experience building scalable models in Looker (LookML) or similar modeling-layer tools.
Automation Mindset: Proven ability to parameterize reports and automate data pipelines to reduce manual intervention.
Education: Bachelor’s degree in Computer Science, Mathematics, Statistics, or a related quantitative field.

Responsibilities

Production-Level Development: Write clean, modular, and performant SQL and Python code. Participate in a rigorous QA environment, ensuring all analytical code is version-controlled and peer-reviewed.
Pipeline Automation: Design, parameterize, and automate recurring data workflows. Transition manual reporting tasks into automated, scalable pipelines that minimize technical debt.
Strategic Reporting & Delivery: Manage the end-to-end delivery of bespoke reporting assets, ensuring data is actionable and delivered through automated schedules.
Self-Service Architecture: Build and maintain scalable data models in Looker. Focus on creating reusable dimensions and measures that empower non-technical users.
Version Control & Governance: Utilize Git for all codebase management, adhering to branching strategies and collaborative development workflows to ensure "single source of truth" reporting.