Staff Data Engineer, Platform Engineering

About Datavant

Datavant is the data collaboration platform trusted for healthcare. We provide critical data solutions for organizations across the healthcare ecosystem—including providers, health plans, researchers, and life sciences companies. Our mission is to make the world's health data secure, accessible and actionable.

The Role

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.

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

Required Experience

• 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)