Resp & Qualifications
PURPOSE:
The purpose of a Sr. Data Analyst is to find meaning in data as a thought leader so that the derived knowledge can be used to make informed decisions and assist the organization in making better business decisions.
ESSENTIAL FUNCTIONS:
- Engages in data exploration using various programming languages (reading and writing code), requiring advanced knowledge of multiple database structures.
- Collect, analyze, and interpret data to glean insights, and develop business appropriate recommendations.
- Research and analyze data flow across complex data ecosystem including operational and analytical data platforms.
- Serve as an expert resource supporting data questions and issues from stakeholders and driving to resolution.
- Lead in the design, development, validation and delivery of data to provide insight into business decisions.
- Manage the collection of business and technical requirements from Stakeholders to deliver data driven solutions. Produces technical documentation that is consistent with professional standards.
- Be a resource and mentor to junior level staff.
SUPERVISORY RESPONSIBILITY:
Position does not have direct reports but is expected to assist in guiding and mentoring less experienced staff. May lead a team of matrixed resources.
QUALIFICATIONS:
Education Level: Bachelor's Degree in Data Science, Mathematics, Computer Science, Statistics, Business or related field OR in lieu of a Bachelor's degree, an additional 4 years of relevant work experience is required in addition to the required work experience.
Experience: 5 years' experience in an analytical/quantitative role.
Preferred Qualifications:
- Experience in Healthcare Industry
- Masters degree in a quantitative field preferred (e.g., Business Analytics, Statistics, Data Science)
Advanced proficiency in:
- SQL for data extraction, transformation, and complex joins
- Power BI (building paginated and interactive dashboards using Power BI Report Builder and Desktop)
- Microsoft Fabric (dataflows, lakehouses, semantic models)
- Intermediate-to-advanced Python skills (pandas, numpy, matplotlib/seaborn) for data wrangling and analysis
- Experience conducting diagnostic and exploratory data analysis (EDA) to identify trends and root causes
- Ability to apply analytical techniques such as segmentation, regression, time-series analysis
- Strong skills in stakeholder communication and translating analysis into business insights
Enterprise Healthcare Analy