Senior Data Analyst

Gifthealth Inc•Columbus, OH

12d•$122,000 - $153,000

About The Position

We are looking for a meticulous Senior Data Analyst with exceptional skills in data analytics and reporting to join our team. This role will serve as a vital link between our business intelligence team, commercial, and client success departments. The Senior Data Analyst will collaborate closely with both internal and client-facing teams to ensure seamless alignment across all analytics initiatives, performance measurement, and key insights. Reporting directly to the Executive Director of Data Strategy, this position is an integral part of the iBIDADS team.

Requirements

Bachelor's degree in Data Analytics, Statistics, or a related field.
10+ years of experience in data analysis, with at least 3 years focused on healthcare reporting.
Advanced skills in SQL and Python, with experience in database management.
Proficiency in data visualization tools such as Tableau, Looker, Metabase, Domo, or similar.
Strong understanding of healthcare metrics and reporting requirements.
Experience supporting commercial and operations teams with program performance and identification of opportunities.
Excellent problem-solving skills and attention to detail.
Deep passion for data, analytics, and a natural curiosity to explore and extract key insights.

Nice To Haves

Experience in the healthcare industry.
Proficiency in R for data manipulation.
Knowledge of data warehouse concepts and ETL processes.
Understanding of SLA management and performance metrics.
Experience working in dual-focused BI environments (internal and client-facing).
Familiarity with pharmacy management systems and logistics data.
Experience with data transfer agreements, data use agreements, and third-party data integration.
Experience with cloud-based data platforms.
Natural curiosity about data and its business implications.
Strong desire to track and improve program performance.
Can-do attitude with a passion for solving complex problems.
Detail-oriented with a commitment to data accuracy and quality.
Excellent communication skills to translate technical findings to business stakeholders.
Self-motivated with the ability to work independently and as part of a team.
Team player willing to go the extra mile to support organizational goals.
Adaptable and responsive during high-pressure periods like launches, business planning, and quarterly reporting.
Commitment to continuous learning and staying updated with industry trends.

Responsibilities

Build, develop, and maintain datasets to support automated reporting solutions and program performance measurement, ensuring alignment with established SLAs.
Support commercial teams with data needs for performance reviews, business planning activities, and newly onboarded business.
Conduct ad hoc analysis to investigate and resolve program issues, providing critical support during QBR, Business Planning, and Launch Execution.
Manage third-party data integration and analysis, including shipping analytics and return-to-sender reconciliation processes.
Design and implement internal and client-facing program performance datasets, tracking and analyzing key SLA measurements and program KPIs.
Co-develop dashboards that visualize program performance trends, providing data-driven insights to client-facing teams and internal operational support.
Collaborate with both internal operations and client-facing BI teams to ensure consistent metrics and reporting.
Provide data-driven insights to the commercial and internal teams.
Partner with internal and customer-facing BI departments on healthcare analytics initiatives, supporting the internal operations excellence team and commercial team with key performance insights.
Ensure consistent data definitions and metrics across both BI functions, contributing to monetization opportunities through consulting and data products.
Work closely with Product Engineering to ensure product features, enhancements, and changes are reflected in the datasets.
Ensure accuracy, reliability, and security of all performance data, implementing data validation processes and quality control measures.
Document data lineage and maintain data dictionaries, collaborating with stakeholders to define business rules and metrics.
Stay updated with industry trends and best practices to continuously improve data management and analytics processes.