Senior/Staff Data Engineer

Our client is seeking a hands‑on Senior or Staff Data Engineer to design, build, and own the data foundation powering an advanced healthtech platform. In this role, you will consolidate complex, longitudinal care data into a unified, high-integrity architecture to empower care teams, optimize user outcomes, and support AI-driven insights at scale. Key Responsibilities Architect and maintain robust data pipelines to aggregate, clean, and standardize fragmented structured and unstructured inputs from diverse external platforms. Collaborate closely with clinical and engineering teams to transform raw operational, longitudinal, and domain data into actionable workflows and clinical tooling. Build and evaluate production‑grade LLM extraction pipelines to surface key insights, automate record synthesis, and enable trial matching while maintaining strict data provenance. Enforce enterprise‑grade data security, governance, and audit standards across all data stores, ensuring privacy, access control compliance, and reliable lineage tracking. Qualifications 5+ years of professional data engineering experience with deep technical expertise in pipeline orchestration, batch/streaming architecture, warehouse design, and data quality modeling. Deep expertise in domain‑specific data standards (e.g., FHIR, HL7, claims data formats, standard clinical terminologies) or integration tools. Strong software engineering fundamentals with experience writing production‑grade code, designing backend schemas, and integrating with external APIs. Hands‑on experience building, evaluating, and deploying LLM systems, specifically focused on structured data extraction from unstructured text. Proven track record of operating data infrastructure in fast‑growing startup environments, balancing rapid delivery with long‑term reliability. Demonstrated experience implementing strict data governance, access controls, minimum‑necessary enforcement, and audit logging for highly sensitive domain data. Ability to work on‑site in New York City (5 days per week). Preferred Qualifications Experience at an early‑stage, VC‑backed startup a plus Experience with modern data infrastructure operations, including Infrastructure as Code (IaC), CI/CD for data pipelines, and cost optimization. Background in analytics engineering, feature pipelines, or supporting machine learning models and real‑world evidence research. #J-18808-Ljbffr Dynamics Solutions, LLC.