Roles & Responsibilities
1. Data Architecture & DataMart Development
- Design, develop, and maintain scalable, analytics-ready datasets for enterprise reporting and operational analytics.
- Lead the development and enhancement of enterprise DataMarts , including ODAG, Timeliness, Risk Adjustment, and other healthcare-focused analytical domains.
- Translate business requirements into robust data models, transformations, and reusable analytical datasets.
- Ensure data solutions align with standardized business definitions, data governance principles, and CMS-driven business logic .
- Collaborate with Managed Care, Finance, Operations, and other business stakeholders to understand, stabilize, and operationalize data requirements.
- Perform data profiling, validation, reconciliation, and quality checks to ensure accuracy and consistency.
2. Data Pipeline & Data Lake Development
- Design, build, optimize, and maintain scalable ETL/ELT data pipelines .
- Develop data solutions using modern data platforms such as Databricks, Azure Data Lake, Snowflake , or similar technologies.
- Integrate data from multiple enterprise and external sources, including Epic Clarity, Tapestry, healthcare claims, laboratory systems, and external vendors .
- Transform and validate large-scale healthcare datasets, including HEDIS, CMS, claims, lab, enrollment, and provider data .
- Automate recurring data workflows and processes to reduce manual intervention and improve data reliability.
- Monitor pipeline performance, troubleshoot data issues, and implement improvements for scalability and maintainability.
3. Business Intelligence & Data Visualization
- Develop and maintain business intelligence dashboards and analytical solutions using Tableau and/or Microsoft Power BI .
- Build dashboards and reporting solutions aligned with enterprise DSS standards, data governance, and visualization best practices .
- Create curated and reusable datasets to enable self-service analytics and reduce dependency on one-off reporting.
- Work closely with business users to translate analytical requirements into meaningful dashboards, KPIs, and actionable insights.
4. AI & Advanced Analytics
- Use Python for data transformation, automation, analytical workflows, and advanced data processing.
- Support the integration of AI/ML and Large Language Model (LLM) capabilities into data and analytics solutions.
- Develop or support LLM-based use cases such as data summarization, classification, insight generation, call center analytics, and CRM analytics .
- Partner with data science, technology, and business teams to develop AI-enabled data products that improve operational decision-making.
- Contribute to the transition toward automated, intelligent data pipelines and AI-enriched datasets .
5. Data Quality, Governance & Compliance
- Implement data validation, reconciliation, and quality checks across data pipelines and analytical datasets.
- Ensure data solutions are accurate, consistent, traceable, and aligned with enterprise data governance standards.
- Support regulatory, compliance, and audit-driven reporting requirements.
- Work with healthcare data and reporting standards related to CMS, HEDIS, Medicare Advantage , and other regulatory requirements.
Candidate Requirements
- 3+ years of professional experience in Data Analytics, Business Intelligence, Analytics Engineering, or Data Engineering.
- Experience working in a healthcare, managed care, payer, or Epic data environment .
- Strong hands-on experience with SQL , including developing and optimizing complex queries and transformations across large datasets.
- Proven experience designing and developing analytics-ready datasets, dimensional models, and enterprise DataMarts .
- Hands-on experience with modern data platforms such as Databricks, Azure Data Lake, Snowflake , or equivalent.
- Strong experience developing and maintaining ETL/ELT pipelines , including data ingestion, transformation, validation, and automation.
- Experience working with Tableau and/or Microsoft Power BI for dashboard development and data visualization.
- Strong proficiency in Python for data processing, automation, and analytics workflows.
- Experience integrating and transforming data from multiple sources and systems.
- Strong understanding of data quality, data governance, data modeling, and analytics best practices .
Preferred Qualifications
- Experience working with Epic Clarity and/or Tapestry .
- Experience with healthcare datasets such as claims, enrollment, provider, EHR, laboratory, HEDIS, and CMS data .
- Experience supporting CMS, HEDIS, Medicare Advantage , regulatory, or audit-driven reporting.
- Exposure to AI/ML, Generative AI, or LLM-based solutions .
- Experience with LLM use cases such as summarization, classification, natural language processing, and insight generation.
- Experience working with call center, CRM, or operational analytics .
- Strong understanding of healthcare payer/managed care business processes.