Staff Data Engineer, Platform Engineering

Datavant is a healthcare data collaboration platform that makes health data secure, accessible, and actionable across the healthcare ecosystem. The Staff Data Engineer will lead the architecture and development of a next-generation patient data platform, building distributed, multi-tenant, multi-cloud data systems and platform capabilities that support analytics, product, and AI-driven use cases.

Responsibilities:

• Lead the architecture and development of core data platform capabilities, including processing frameworks, storage patterns, and shared services
• Design and implement multi-tenant, multi-cloud data systems with strong isolation, scalability, and operational durability
• Build and operate large-scale distributed data processing systems across batch and real-time workloads
• Define and evolve data lifecycle patterns, including ingestion, validation, transformation, enrichment, and serving
• Establish data quality gates and validation frameworks to ensure trust, consistency, and auditability
• Design systems that integrate with platform infrastructure, including CI/CD, deployment orchestration, observability, and infrastructure automation
• Make sound architectural decisions across performance, cost, reliability, and maintainability tradeoffs
• Lead ambiguous, high-impact initiatives where both problem definition and solution design require ownership
• Contribute significantly to production code, setting standards for quality, testing, and operability

Requirements:

• • Strong candidates will have experience with several of the following:
• • Distributed data processing frameworks (e.g., Spark, Flink, or similar)
• • 10+ years of experience building data-intensive or distributed systems, with a strong software engineering foundation
• • Proven experience designing and operating large-scale data platforms in production
• • Deep expertise in distributed data processing systems (e.g., Spark or similar big data technologies)
• • Strong software engineering fundamentals, including system design, testing, CI/CD, and production debugging
• • Experience building systems in cloud environments (AWS preferred), including storage, compute, and security patterns
• • Experience designing multi-tenant systems, with a focus on isolation, scalability, and reliability
• • Strong understanding of data modeling, pipeline design, and data quality enforcement
• • Ability to navigate ambiguity, evaluate tradeoffs, and drive durable technical decisions
• • Track record of being a high-impact, hands-on contributor who leads through both design and execution
• • Cloud data platforms (e.g., Databricks, Snowflake, or equivalent)
• • Data transformation and modeling frameworks (dbt or equivalent)
• • Workflow orchestration systems (e.g., Airflow or similar)
• • Streaming and event-driven systems (e.g., Kafka or equivalent)
• • Infrastructure-as-code (e.g., Terraform)
• • Modern table formats and lakehouse architectures (e.g., Iceberg, Delta, or similar)
• • Experience building data systems that support AI-driven use cases, including:
• • low-latency data access patterns
• • feature generation and ML data pipelines
• • iterative, feedback-driven data workflows
• • Familiarity with agentic or AI-assisted coding tools, and the ability to leverage them to improve development velocity and code quality
• • Comfort operating in environments where AI augments both system design and development workflows
• • Experience in regulated environments (e.g., healthcare, finance)
• • Familiarity with interoperability standards (e.g., FHIR, HL7, or similar)
• • Experience leading large-scale platform migrations or architectural transformations