• Design and implement data pipelines (ETL/ELT) using modern tools (e.g., Apache Airflow, DBT, Dataflow); • Integrate data from transactional systems, APIs, and relational and non-relational databases; • Create and maintain optimized data structures in analytical environments (data lakes and data warehouses); • Ensure data governance, data quality, and data cataloging; • Automate routines for data extraction, transformation, and loading; • Support data scientists, analysts, and product squads with reliable, well-modeled data; • Participate in modernization and data migration projects to the cloud; • Monitor and resolve failures in pipelines and other critical data processes.

• Supports ECS/EAD (Enterprise Case Selection/Enterprise Anomaly Detection) data engineering tasks including ingestion scripting, basic transformation logic, and data preparation activities for analytics and operational workloads. • Assists senior engineers in building and maintaining pipelines, validating data accuracy, and troubleshooting low‑complexity ingestion errors. • Performs initial schema mapping, data profiling, and documentation updates. • Works with analysts and data scientists to ensure datasets are complete, accessible, and aligned with ECS/EAD mission and compliance requirements

• Supports ECS/EAD (Enterprise Case Selection/Enterprise Anomaly Detection) data engineering efforts with a focus on automation and repeatable data processing workflows. • Builds automated data validation routines, ETL test harnesses, and monitoring scripts to ensure pipeline reliability, data integrity, and compliance with agency standards. • Implements ingestion and transformation components, integrates with cloud data services, and resolves pipeline defects through automated checks. • Collaborates with senior engineers, analysts, and cloud teams to ensure data flows are accurate, secure, and aligned with ECS/EAD modernization patterns.

• Transform, model, and integrate raw data of varying quality from a wide range of data sources into usable, documented and high quality data and intelligence products by applying data modeling and statistical techniques. • Identify, research, and develop new statistical approaches and new types of data to improve and extend our core healthcare data products. • Lead feature and product development cycles from defining Customer problem statements through to delivering solutions. • Work directly with product managers and cross-functional stakeholders to influence and build our Product development plans and roadmap. • Provide thought leadership on data science techniques and mentoring to junior data engineers. • Document and communicate technical and quantitative concepts, schemas, and data product usage guidelines with appropriate levels of detail for internal and external stakeholders.