Senior Business Intelligence Analyst

Position Description: The Senior Business Intelligence Analyst will bring deep expertise in Power BI (or similar BI tools), advanced proficiency in SQL and Python, and a command of statistical analysis, along with the ability to translate complex healthcare data into strategic insights. This role requires someone who can work independently, mentor junior analysts, and partner closely with clinical, operational, and business leaders to drive data-informed decision-making. Position Responsibility: • Leads the design, development, and maintenance of advanced dashboards and reports using Power BI (or Tableau/Looker/Qlik) to support clinical operations, quality, finance, and population health initiatives • Writes and optimizes complex SQL queries across large healthcare datasets (EHR/EMR, claims, clinical, operational data) • Analyzes healthcare-specific metrics such as patient outcomes, readmission rates, length of stay, utilization, cost-of-care, and quality measures (HEDIS, CMS Star Ratings, etc.) • Ensures compliance with healthcare data regulations (HIPAA, HITECH) and data governance best practices in all analyses and reporting • Partners with clinical, operations, finance, and executive leadership to define KPIs, identify data needs, and deliver actionable insights • Uses Python (Pandas, NumPy, SciPy, scikit-learn) for advanced data wrangling, automation, and predictive/statistical modeling • Applies advanced statistical techniques (hypothesis testing, regression, survival analysis, risk adjustment models, cohort analysis) to evaluate clinical outcomes, cost trends, and operational performance • Leads root-cause analysis on data quality issues, workflow inefficiencies, and performance gaps within clinical/operational processes • Mentors and provides technical guidance to junior/mid-level analysts on best practices in SQL, Python, and dashboard design • Drives automation of recurring reports and process improvements to enhance data pipeline efficiency • Collaborates with data engineering teams on data architecture, ETL pipelines, and data warehouse design as needed Position Qualification: • Bachelor's degree in data science, statistics, health informatics, computer science, or related field. • Advanced proficiency in Power BI or similar BI/visualization tools (Tableau, Looker, Qlik Sense), including DAX and data modeling • Expert-level SQL skills (complex joins, window functions, CTEs, query optimization, stored procedures) • Strong Python skills for data analysis and statistical modeling (Pandas, NumPy, SciPy, scikit-learn) • Advanced understanding of statistical methods (regression analysis, hypothesis testing, A/B testing, predictive modeling) • Familiarity with healthcare data standards and terminologies (ICD-10, CPT, HCPCS, HL7/FHIR) is highly desirable • Working knowledge of HIPAA and healthcare data privacy/compliance requirements • Proven ability to lead projects end-to-end and manage stakeholder relationships independently • Excellent communication and storytelling skills — able to translate technical/clinical data into insights for non-technical executives. PROFESSIONAL EXPERIENCE: • 5+ years of experience as a Data Analyst, with significant experience in the healthcare industry (payer, provider, health system, or health-tech) • Experience with claims data, EHR/EMR systems (Epic, Cerner, etc.), or clinical data warehouses • Exposure to value-based care, population health management, or quality reporting programs (HEDIS, MIPS, Star Ratings) • Experience with cloud platforms (Azure, AWS, or GCP) and modern data warehousing (Fabric Snowflake, Redshift, BigQuery) • Familiarity with machine learning applications in healthcare (risk prediction, readmission models) • Experience with version control (Git) and Agile/Scrum methodologies • Prior experience mentoring or leading a small analytics team. Show more Show less

Bachelor's degree in data science, statistics, health informatics, computer science, or related field. Advanced proficiency in Power BI or similar BI/visualization tools (Tableau, Looker, Qlik Sense), including DAX and data modeling. Expert-level SQL skills (complex joins, window functions, CTEs, query optimization, stored procedures). Strong Python skills for data analysis and statistical modeling (Pandas, NumPy, SciPy, scikit-learn). Advanced understanding of statistical methods (regression analysis, hypothesis testing, A/B testing, predictive modeling). Familiarity with healthcare data standards and terminologies (ICD-10, CPT, HCPCS, HL7/FHIR) is highly desirable. Working knowledge of HIPAA and healthcare data privacy/compliance requirements. Proven ability to lead projects end-to-end and manage stakeholder relationships independently. Excellent communication and storytelling skills — able to translate technical/clinical data into insights for non-technical executives.

Leads the design, development, and maintenance of advanced dashboards and reports using Power BI (or Tableau/Looker/Qlik) to support clinical operations, quality, finance, and population health initiatives. Writes and optimizes complex SQL queries across large healthcare datasets (EHR/EMR, claims, clinical, operational data). Analyzes healthcare-specific metrics such as patient outcomes, readmission rates, length of stay, utilization, cost-of-care, and quality measures (HEDIS, CMS Star Ratings, etc.). Ensures compliance with healthcare data regulations (HIPAA, HITECH) and data governance best practices in all analyses and reporting. Partners with clinical, operations, finance, and executive leadership to define KPIs, identify data needs, and deliver actionable insights. Uses Python (Pandas, NumPy, SciPy, scikit-learn) for advanced data wrangling, automation, and predictive/statistical modeling. Applies advanced statistical techniques (hypothesis testing, regression, survival analysis, risk adjustment models, cohort analysis) to evaluate clinical outcomes, cost trends, and operational performance. Leads root-cause analysis on data quality issues, workflow inefficiencies, and performance gaps within clinical/operational processes. Mentors and provides technical guidance to junior/mid-level analysts on best practices in SQL, Python, and dashboard design. Drives automation of recurring reports and process improvements to enhance data pipeline efficiency. Collaborates with data engineering teams on data architecture, ETL pipelines, and data warehouse design as needed.