Analyze large amounts of healthcare data from multiple sources to support business decision making
Perform exploratory data analyses and evaluate data quality and usability.
Assist in the development and maintenance of ETL/ELT pipelines using established templates and internal tools.
Perform routine data audits to flag quality issues, develop reports to monitor pricing quality and coverage, and assist in developing workarounds to data limitations.
Assist in gathering data requirements by participating in stakeholder meetings and documenting business needs.
Work closely with team members to identify key metrics and understand the "why" behind data requests.
Document existing data workflows to help the team identify inefficiencies or error prone areas (available tools include Miro, Confluence, and Jira)

Bachelor's degree ideally in a quantitative field, such as statistics, mathematics, economics, or computer science.
1-2 years of experience using Python (pandas/numpy) for data analysis, including composing robust data processing code to enforce expected patterns, handle exceptions, and evaluate data usability/quality through exploratory data analyses.
Exposure to large-scale datasets; experience with healthcare claims or administrative data is a major plus but not required.
Ability to perform descriptive analysis and translate findings into clear charts or summaries for the team.
Excellent communication skills, with the ability to translate complex data into clear and actionable insights.
Strong organizational skills, with the ability to work on multiple projects and priorities in a fast-paced environment.
Basic familiarity with Git for version control (e.g., branching, committing, pulling code, and submitting pull requests).

Data Analyst

Key skills