• Lead technical data discussions with health plan clients to understand data environments and define how client data should map to Siftwell’s platform. • Serve as the primary technical counterpart in client onboarding meetings alongside Customer Success, using deep familiarity with healthcare data standards (EDI 834/837, claims, eligibility, clinical data) to guide productive discussions. • Evaluate client data delivery approaches and guide clients toward formats that align with Siftwell’s platform data model. • Build and maintain a standardized onboarding data playbook defining required data formats, field mappings, and delivery expectations for new clients. • Identify data availability gaps early and work with clients and internal teams to define workarounds or phased implementation approaches. • Own Siftwell’s data model roadmap, including the gold-layer schema, condition classification mappings (e.g., AHRQ CCSR), population definitions, and derived metrics. • Define and maintain canonical data specifications that translate healthcare domain knowledge into engineering-ready documentation for areas such as condition mapping, cost calculation logic, quality measure dependencies, and eligibility processing. • Define specifications for new data capabilities as the platform expands into additional healthcare domains (e.g., foster care, D-SNP, behavioral health), including relevant regulatory and definitional requirements. • Define data quality, governance, and validation frameworks that incorporate domain-informed validation of clinical and operational logic. • Write PRDs and technical specifications for data model changes, new data integrations, and pipeline enhancements. • Operate in a pod structure with the data engineering team, owning prioritization and specifications while engineering owns execution and architecture. • Partner with Product leadership to ensure data model decisions align with overall platform strategy and roadmap priorities. • Collaborate with Customer Success to distinguish between onboarding tasks, product feature requests, and out-of-scope client requests. • Translate patterns observed across client onboardings into product improvements — turning recurring bespoke work into repeatable platform capabilities. • Communicate data architecture decisions and onboarding requirements clearly to both technical and non-technical stakeholders, including client executives.