Informatica IICS Developer

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.