• Work closely with Medisolv clients to extract, transform and load clinical, financial and administrative healthcare data and health plan data. • Assist in loading and troubleshooting data issues from EHR vendor products (including but not limited to: Epic, Meditech, Cerner and EClinicalWorks). • Support CMS quality measures (HEDIS/Stars) and fuel downstream AI workflows. • Integrate with the data management team and become familiar with workflows and tools in Azure and AWS environments (Snowflake, Databricks, Data Factory, Glue, Lambda, SQL Server). • Review existing data pipelines, data models, and transformation scripts to support troubleshooting. • Investigate technical and data-related issues affecting measure results and mapping (e.g., SNOMED, ICD-10, RXNORM). • Apply SQL querying skills to investigate data anomalies within Databricks and Snowflake environments. • Troubleshoot data pipelines to support internal operations and client deliverables. • Perform data analysis to identify trends, discrepancies, and opportunities for improvement. • Collaborate with cross-functional teams to gather requirements and enhance data quality and reporting outputs. • Develop and deliver dashboards, reports, and visualizations that support business and client decisions. • Monitor data quality, troubleshoot issues, and drive improvements in data integrity and consistency. • Partner with stakeholders to translate data into actionable insights aligned with business goals. • Lead enhancements to data architecture, reporting capabilities, and data processes. • Support advanced analytics initiatives, including predictive insights and AI-driven solutions. • Document processes and establish best practices to ensure scalability, reliability, and consistency as the organization grows.

• Develop, construct, and maintain large-scale data processing systems that collect data from a variety of structured and unstructured sources — using AI code generation tools to accelerate pipeline authoring, reduce boilerplate, and improve code quality. • Build and optimize ELT pipelines using AI-assisted tooling to identify bottlenecks, suggest optimizations, and automate routine pipeline maintenance tasks. • Identify, design, and implement internal process improvements: use AI to automate manual processes, optimize data delivery, and re-design infrastructure for greater scalability — replacing manual analysis with AI-driven discovery of improvement opportunities. • Build the infrastructure required for optimal extraction, transformation, and loading of data from various sources; use AI to accelerate infrastructure-as-code authoring and configuration. • Prepare data for data scientist exploration and discovery using AI-assisted data profiling and quality assessment tools — surfacing anomalies, schema drift, and data gaps faster than manual inspection allows. • Perform data wrangling and munging for downstream analytics and machine learning; leverage AI tools to generate and validate transformation logic against business rules. • Assemble large, complex datasets that meet functional and non-functional business requirements; use AI to rapidly evaluate dimensional modeling approaches and ontology alignment strategies. • Enable large-scale machine learning by designing and maintaining annotated datasets, elastic search approaches, and scalable data lake structures that support AI/ML workloads. • Create and maintain analytics pipelines that generate data and insight to power business decision-making; use AI-assisted analysis to proactively surface trends, anomalies, and opportunities within pipeline outputs. • Collaborate with data scientists, analysts, and business stakeholders on requirements for dimensional modeling, distributed ETL pipelines, and cross-repository data migration. • Evaluate, compare, and improve design patterns, data lifecycle approaches, and data ontology alignment — using AI to model trade-offs and accelerate proof-of-concept validation. • Work with data and analytics experts to continuously improve the functionality, reliability, and intelligence of data systems. • Perform root cause analysis on internal and external data and processes using AI-assisted investigation tools — replacing slow, manual log and lineage review with faster, AI-accelerated diagnostics. • Develop and maintain data quality frameworks; use AI to automate anomaly detection, schema validation, and data contract enforcement across pipelines. • Develop a strong understanding of company domains, strategic direction, and user needs to ensure data systems are aligned to business outcomes, not just technical requirements.

• Define and maintain enterprise data architecture standards across structured, semi-structured, and unstructured data domains. • Use AI-assisted modeling tools to accelerate data model design and validate designs against business requirements. • Design and govern the organization's cloud data lake, data warehouse, and lakehouse architectures on AWS. • Establish data ontology, taxonomy, and semantic layer standards that enable AI systems to reason over organizational data accurately. • Evaluate emerging data architecture patterns and build a roadmap for their adoption across Stellus Rx. • Design scalable data models and ELT/ETL pipeline architectures that support analytics and AI/ML workloads. • Define standards for data partitioning, indexing, caching, and storage optimization. • Partner with Data Engineers to translate architectural blueprints into production-ready pipelines. • Define and enforce data governance frameworks, data quality standards, and data contracts across the enterprise. • Develop and maintain a master data management (MDM) strategy.

• Design, develop, and maintain scalable data pipelines for ingestion, transformation, and delivery of large datasets across diverse industries. • Implement and ensure data privacy and security best practices, supporting data sovereignty and compliance with regulatory requirements. • Collaborate closely with AI/ML engineers, Data Scientists, and Platform engineers to enable advanced analytics and AI capabilities while retaining strict data control. • Optimize data platforms and systems for performance, reliability, and cost efficiency. • Build tools and frameworks for secure, privacy-preserving data processing and orchestration. • Develop and maintain documentation, data models, and technical workflows. • Partner with cross-functional teams to launch new data-driven product features and solutions.