Senior​/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.

• 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.

• 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).

• 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.