Staff Data Engineer, Platform Engineering

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