• Design, build, and maintain scalable and resilient data pipelines using Spark, Presto, and SQL • Develop high-quality ETL workflows to ingest, transform, and aggregate data from multiple upstream sources • Partner closely with data analysts, finance partners, and product teams to understand business needs • Design and evolve data models that accurately represent financial entities and member behavior • Champion data quality by implementing checks, validations, and monitoring • Monitor, troubleshoot, and optimize data workflows for performance and maintainability • Contribute to improving data engineering best practices, tooling, and documentation

• Building efficient, critical data pipelines and data platform • Collaborate closely with ML/AI Engineers, Backend Engineers and product to create the data sets that power vidIQ’s algorithms • Be an advocate for data quality, acquisition of new data sources, and data infrastructure tooling • Work closely with cross-functional teammates, including product managers, designers, backend, DevOps and ML/AI Engineers to deliver the highest impact to our users

• Provides expertise in designing, developing, and maintaining scalable data pipelines and architectures supporting enterprise data catalogs and federated data environments • Integrates structured and unstructured data from clinical, operational, and research systems (e.g., EHRs, data lakes, APIs) • Implements metadata harvesting, data lineage, and data quality enforcement aligned with enterprise governance frameworks • Supports AI/ML readiness through feature engineering, data transformation, and pipeline automation • Works within federal healthcare environments, ensuring compliance with HIPAA, DoD, and DHA data standards

• Design, implement, and maintain scalable data pipelines to collect, process, and store data from various sources. • Ensure data quality, accuracy, and consistency throughout the pipeline. • Design and implement existing data models for predictive analytics, machine learning, and data exploration. • Optimize data structures and storage to support predictive analytics/machine learning processes. • Work closely with cross-functional teams to integrate data from diverse sources, including databases, APIs, and external data providers. • Develop and maintain ETL processes to transform and enrich raw data into actionable insights. • Monitor and optimize the performance of data pipelines and databases to meet business requirements. • Stay up-to-date with the latest advancements in data engineering and data science technologies. • Share knowledge and mentor junior team members.