Staff Data Engineer, Platform Engineering

Datavant is the data collaboration platform trusted for healthcare. Guided by our mission to make the world’s health data secure, accessible and actionable, we provide critical data solutions for organizations across the healthcare ecosystem - including providers, health plans, researchers, and life sciences companies. From fulfilling a single patient’s request for their medical records to powering the AI revolution in healthcare, Datavanters are building the future of how data is connected and used to improve health. By joining Datavant today, you’re stepping onto a driven and highly collaborative team that is passionate about creating transformative change in healthcare. What We’re Looking For: As a Staff Data Engineer at Datavant, you will lead the design and build of our next-generation patient data platform, developing the distributed data systems and platform capabilities that power secure, scalable, and intelligent use of data across a multi-tenant, multi-cloud environment. This is a hands-on technical leadership role for a software-oriented data engineer who combines strong architectural judgment with deep implementation expertise. You will define how complex data is processed, validated, and served—supporting analytics, product, and AI-driven use cases in a regulated environment. What You Will Do: 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 Technical Experience: Strong candidates will have experience with several of the following: Distributed data processing frameworks (e.g., Spark, Flink, or similar) 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) What You Need to Succeed: 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 What Helps You Stand Out: Experience building data systems that support AI-driven use cases, including: low-latency data access patterns feature generati…