• 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.

• Design, build, and operate data collection and analysis pipelines • Work with large-scale internet measurement data (we collect 75+ TB per week , including BGP, DNS, ping, and traceroute data from 1200+ global vantage points ) • Research, apply, and implement techniques from cutting-edge internet measurement research • Maintain a high bar for signal quality and defensibility , prioritizing observable network behavior over heuristics or guesswork • Communicate findings clearly by contributing to blog posts, technical documentation, and research publications , both internally and externally

• Utilize extract/transform/load ETL technologies using snowflake and other cloud data platforms • Interpret data, analyze results using statistical techniques and provide ongoing reports • Develop and implement databases, data collection systems, data analytics, and other strategies that optimize statistical efficiency and quality • Acquire data from primary or secondary data sources and maintain databases/data systems • Evaluate and optimize data structures • Identify, analyze, and interpret trends or patterns in complex data sets • Filter and “clean” data by reviewing computer reports, printouts, and performance indicators to locate and correct code problems • Monitor, troubleshoot, and improve pipeline transparency, performance, scalability, and reliability, using Snowflake OpenFlow and related ELT/ETL tools • Ensure AI/ML readiness of data by preparing and maintaining semantic models, ensuring robust data quality, and establishing and enforcing data access • Produce field mapping and translation documentation for use in both manual and scripted migrations • Work within Agile methodology managing tasks and tickets as assigned • Communicate with clients and team members for requirements gathering, clarification, and planning for data conversions • Document work and work processes for use by team members