• Implementation of the Data Science models delivery pipeline to Production in collaboration with Data Science • Integration and collection of data from different internal and external sources into a single Data Warehouse (ETL procedures) • Improving infrastructure for experimentation, storage of results, retraining of models, quality monitoring, and alerting • Automation of data collection and analysis processes • Making architectural decisions when developing a data storage and processing system • Monitoring of data sources • Interaction with analysts and programmers in the process of designing and implementing tasks

• Build and maintain robust, scalable data pipelines using Airflow, Python, and SQL. • Design, build, optimize data pipelines using dbt, or sqlmesh. • Develop and manage the ODS and support the eventual rollout of a modern Data Warehouse. • Integrate data from internal systems and external APIs to create clean, reliable datasets. • Work closely with engineers to operationalize machine learning workflows. • Ensure high data quality through monitoring, validation, and error handling. • Provide guidance to less experienced team members and champion data engineering best practices. • Deploy and manage infrastructure in the cloud (AWS, GCP, or Azure) using modern DevOps tooling. • Implement monitoring and alerting to ensure data pipelines are reliable and maintainable.

• Conduct a comprehensive review of the existing data ecosystem, including spreadsheets, CRM platforms, and operational systems • Define and design a “single source of truth” for employee/agent data and associated business metrics • Architect a scalable data framework, including core data models, system integrations and data flows, governance, ownership, and data quality standards • Provide recommendations on appropriate technologies (databases, data warehouses, integration tools, and SaaS vs custom solutions) • Produce clear and robust documentation, including target-state architecture diagrams, data models and definitions, integration and migration roadmap • Act as a strategic advisor to senior leadership and technical teams throughout the transformation journey

• Design, build, and implement data systems to support ML and AI models for our health insurance clients, ensuring strict compliance with healthcare data privacy and security regulations (e.g., HIPAA). • Develop tools for extracting, processing, and profiling diverse healthcare data sources, including EHRs, medical claims, pharmacy data, and genomic data. • Collaborate with data scientists to transform large volumes of health-related and bioinformatics data into modeling-ready formats, prioritizing data quality, integrity, and reliability in healthcare applications. • Build and maintain infrastructure for the extraction, transformation, and loading (ETL) of data from a variety of sources using SQL, AWS, and healthcare-specific big data technologies and analytics platforms. • Ensure data pipelines meet the unique requirements of health, medical, and bioinformatics data processing, including translating complex medical and biological concepts into actionable data requirements.