Principal Data Analyst

GI Alliance•Southlake, TX

33d

About The Position

The Principal Data Analyst leads advanced data analysis initiatives that support strategic decision-making, improve patient outcomes, and optimize operational efficiency across the Specialty Alliance ecosystem: Driving Data-Informed Strategy Lead complex analytical projects that inform clinical, operational, and financial strategies. Translate healthcare data into actionable insights for senior leadership. Develop predictive models, risk stratification tools, and other advanced analytics to support population health, cost containment, and quality improvement. Ensure data accuracy, consistency, and compliance with healthcare regulations (e.g., HIPAA). Collaborate with data engineering and architecture teams to maintain robust data infrastructure. Healthcare Domain Knowledge: Leverage deep understanding of healthcare systems, clinical workflows, and regulatory requirements (e.g., HIPAA, CMS). Analyze EMR, claims/billing data, patient outcomes, and population health data. Collaboration and Communication: Partner with clinical, financial, and operational teams to identify data needs and deliver insights. Communicate findings clearly to both technical and non-technical audiences. Collaborating with Data Architects and Data Engineers in the integration of new sources Working directly with external vendors to understand their requirements and translate those requirements into source-to-target mappings. Data Quality and Governance: Ensure data integrity, accuracy, and compliance with healthcare standards. Contributes to data governance frameworks and best practices.

Requirements

Bachelor's degree in computer science, Data Science, Health Informatics, Statistics, Public Health, Mathematics, Economics or an alternate program with equivalent Data Analysis experience.
10+ years of professional experience in data analytics, with at least 7 years specifically in healthcare analytics.
Experience leading complex analytic projects in clinical, operational, or payer environments.
Advanced proficiency in SQL, experience in Python coding and API integrations.
Experience with ETL processes and tools (e.g. Matillion, SSIS, Informatica), data warehousing (e.g., Snowflake, Redshift, BigQuery) and experience with structured and unstructured data (e.g. EMR, claims, HL7, 835/837 files).
Electronic Medical Records (EMR/EHR) systems (e.g. Epic, Allscripts, Modmed, ECW)
Claims data and payer systems
Healthcare data standards: ICD, CPT, LOINC, Pharmacy data and Provider Taxonomy Codes
HIPAA compliance and data privacy practices
Creating source-to-target mapping (STM) documentation, understanding data types and sizing, and representing relational tables using data modeling tools and MS Excel.
Leading cross-functional analytic projects
Collaborating with clinical, IT and business teams
Mentoring junior team members and contributing to data strategy
Excellent problem-solving skills and attention to detail.
Strong communication and collaboration skills.
Development of financial data integrations and mappings from legacy to target schemas. Demonstrate conceptual knowledge of period accounting data, how to collect, present and validate charges, refunds, payments, voids to balance a period from the legacy system to a reporting system.
Development of medical data integrations and mappings from disparate file(s) into a target database schema. Demonstrate detailed knowledge of medical data, periods of care, CPT codes, encounters, service dates, primary/secondary insurance carriers, payment data, locations of service and providers.

Nice To Haves

Deep understanding of healthcare workflows, payer-provider dynamics, and regulatory requirements (e.g., HIPAA, CMS)
Experience analyzing claims, clinical, and population health data.
Experience working with Agile tools such as Jira and Confluence
Over 7 years of experience collaborating with data engineering teams.
In depth data validation techniques.

Responsibilities

Lead complex analytical projects that inform clinical, operational, and financial strategies.
Translate healthcare data into actionable insights for senior leadership.
Develop predictive models, risk stratification tools, and other advanced analytics to support population health, cost containment, and quality improvement.
Ensure data accuracy, consistency, and compliance with healthcare regulations (e.g., HIPAA).
Collaborate with data engineering and architecture teams to maintain robust data infrastructure.
Leverage deep understanding of healthcare systems, clinical workflows, and regulatory requirements (e.g., HIPAA, CMS).
Analyze EMR, claims/billing data, patient outcomes, and population health data.
Partner with clinical, financial, and operational teams to identify data needs and deliver insights.
Communicate findings clearly to both technical and non-technical audiences.
Collaborating with Data Architects and Data Engineers in the integration of new sources
Working directly with external vendors to understand their requirements and translate those requirements into source-to-target mappings.
Ensure data integrity, accuracy, and compliance with healthcare standards.
Contributes to data governance frameworks and best practices.