• Collaborate with the Team Lead and cross‑functional teams to gather and refine data requirements for Denials AI solutions • Design, implement, and optimize ETL/ELT pipelines using Python, Dagster, DBT, and AWS data services (Athena, Glue, SQS) • Develop and maintain data models in PostgreSQL; write efficient SQL for querying and performance tuning • Monitor pipeline health and performance; troubleshoot data incidents and implement preventive measures • Enforce data quality and governance standards, including HIPAA compliance for PHI handling • Conduct code reviews, share best practices, and mentor junior data engineers • Automate deployment and monitoring tasks using infrastructure-as-code and AWS CloudWatch metrics and alarms • Document data workflows, schemas, and operational runbooks to support team knowledge transfer

• Design, develop, and maintain ETL/ELT workflows using Oracle Data Integrator (ODI) • Create, optimize, and troubleshoot PL/SQL packages, procedures, functions, triggers, and complex SQL queries • Develop and support data integration, data migration, and data transformation processes • Perform performance tuning of ODI interfaces, mappings, and Oracle database objects • Analyze business requirements and translate them into technical specifications and ETL designs • Monitor and resolve production issues, ensuring data accuracy and timely delivery • Implement data quality checks, validation rules, and error-handling mechanisms • Collaborate with business analysts, data architects, and stakeholders during project delivery • Participate in code reviews, testing, deployment, and documentation activities • Ensure adherence to data governance, security, and development standards

• Own our image and video dataset lifecycle: taxonomy, curation, collection workflows, and quality, so our models train on clean, well-structured, error-free data. • Design and maintain the data pipelines that move, transform, version, and sync our datasets (e.g. DVC, annotation platforms, cloud storage). • Build internal tools and software that make dataset creation, labeling, and curation faster and more reliable for the ML and labeling teams. • Coordinate closely with our labeling team to ensure data is well curated, consistently annotated, and error-free. • Improve how we collect and prioritize training data over time, including model-assisted and active-learning approaches.

• Design, build, and maintain batch, incremental, and file-based data pipelines to support analytics, reporting, and operational use cases. • Support data migration initiatives, including the movement of data from legacy systems to modern platforms, ensuring continuity, accuracy, and reconciliation. • Work with enterprise data integration tools such as Talend, including maintaining existing Talend jobs and supporting the migration or re-implementation of Talend pipelines into other platforms where required. • Establish and maintain a robust data engineering environment (e.g., dev/test/prod separation, access controls, naming standards, source control, deployment processes, and monitoring) that enables reliable delivery and safe change management. • Support ingestion, storage, and management of unstructured and semi-structured datasets (e.g., documents, PDFs, text extracts, files, metadata) alongside traditional relational data. • Support and optimize data models in Power BI dashboards and AI-enabled analytics. • Prepare and curate analytics- and AI-ready datasets, including clean, well-defined tables and reference data used in automation and AI-enabled solutions. • Collaborate closely with AI & Automation Specialists to ensure data requirements for AI initiatives are well understood, properly sourced, validated, and production-ready. • Translate business and reporting requirements into scalable data models, transformations, and pipeline designs. • Develop and maintain trusted datasets that support dashboards, scorecards, client reporting, and downstream analytics. • Implement data quality checks, validation logic, reconciliation processes, and monitoring to ensure data reliability and consistency. • Troubleshoot and resolve data issues across ingestion, transformation, and consumption layers, identifying root causes and remediation actions. • Develop and maintain clear documentation, including source-to-target mappings, data definitions, data lineage, and known limitations. • Work in partnership with analytics, IT, data governance, and security stakeholders to ensure data solutions comply with privacy, security, and regulatory expectations. • Contribute to the prioritization, planning, and delivery of multiple data engineering, migration, and enablement initiatives in parallel.