• Build and maintain data pipelines that support ingest, logging, validation, transformation, and secure data access. • Contribute to the development and improvement of scalable ETL/ELT processes under guidance from senior engineers. • Implement and evolve data models and storage patterns that support analytics and operational use cases. • Apply data quality checks, monitoring, and documentation to ensure data is accurate, reliable, and well understood. • Collaborate with internal teams and clients to understand data requirements and support analytics use cases. • Contribute to the ongoing development of Expression’s VOR Data Platform, including tooling, standards, and automation. • Participate in research, prototypes, and proof-of-concept work to evaluate new data and analytics technologies, including emerging AI-driven tools. • Follow established engineering practices and contribute ideas to improve reliability, performance, and developer experience.

• Design, maintain, and validate data schemas supporting federation and integration between C2SET and external systems • Build and support SQL and Python based ETL pipelines for operational, simulation, and analytics data • Ensure data integrity, correctness, and performance across distributed and multi tier data sources • Troubleshoot data mismatches, malformed messages, schema drift, and integration issues • Partner with modeling and simulation engineers to analyze simulation outputs and support tuning of behaviors and decision logic • Design and execute data driven experiments to evaluate model changes and operational impacts • Develop datasets, scripts, and tooling to support repeatable validation and performance analysis • Support Government analysts and integrators by delivering timely, reliable data refreshes and analysis • Perform database performance tuning and optimization for operational workloads • Maintain data documentation, metadata repositories, and data governance artifacts • Act as a functional lead for data engineering and analytics activities within the integrations team

• Own end-to-end data migrations for new and expanding customers • Import and validate patient, appointment, invoice, payment, provider, service, and membership data • Map data from a wide range of legacy systems (EMRs, POS tools, spreadsheets, exports) • Identify, clean, normalize, and reconcile inconsistent or incomplete data • Perform QA checks to ensure data accuracy, completeness, and integrity post-migration • Work directly with customers during onboarding to define migration scope and timelines • Explain data requirements, limitations, and tradeoffs in a clear, customer-friendly way • Support go-live readiness by ensuring migrated data aligns with customer workflows • Troubleshoot and resolve migration issues quickly and accurately • Maintain and improve migration playbooks, templates, and checklists • Document repeatable patterns for common legacy systems • Partner with Engineering and Product to improve migration tooling and automation • Surface recurring data issues and upstream product improvements • Partner closely with Customer Experience and Implementation teams on go-live execution • Coordinate with Engineering on complex migrations or edge cases • Provide internal visibility into migration status, risks, and blockers

• Act as the transition point between Prompt Engineering and Data Labeling, translating model and product requirements into concrete data and annotation workflows. • Design, implement, and maintain scalable data workflows for dataset generation, curation, and ongoing maintenance. • Ensure data quality and consistency across labeling projects, with a focus on operational reliability for production AI systems. • Create, review, and maintain high-quality annotations across multiple modalities, including text, audio, conversational transcripts, and structured datasets. • Identify labeling inconsistencies, data errors, and edge cases; propose and enforce corrective actions and improvements to annotation standards. • Utilize platforms such as Labelbox, Label Studio, or Langfuse to manage large-scale labeling workflows and enforce consistent task execution. • Use Python and SQL for data extraction, validation, transformation, and workflow automation across labeling pipelines. • Leverage LLMs (e.g., GPT-4, Claude, Gemini) for prompt-based quality checks, automated review, and data validation of annotation outputs. • Implement automated QA checks and anomaly-detection mechanisms to scale quality assurance for large datasets. • Analyze annotation performance metrics and quality trends to surface actionable insights that improve labeling workflows and overall data accuracy. • Apply statistical analysis to detect data anomalies, annotation bias, and quality issues, and partner with stakeholders to mitigate them. • Collaborate with ML and Operations teams to refine labeling guidelines and enhance instructions based on observed patterns and error modes. • Work closely with Prompt Engineering, Data Labeling, and ML teams to ensure that data operations align with model requirements and product goals. • Document data standards, annotation guidelines, and workflow best practices for use by internal teams and external labeling partners.