Senior Site Reliability Engineer

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.

We’re looking for a Senior Site Reliability Engineer to join our Data & ML Platform team. You’ll be at the forefront of building and operating a resilient, observable, and scalable platform that enables mission-critical data and ML workloads across our organization.

This role is ideal for someone who combines a strong SRE mindset with deep cloud infrastructure and data platform experience . You're comfortable operating at scale in a complex, hybrid cloud environment and can architect systems that balance velocity, safety, and cost. You’ll work closely with Data & ML Engineers, Data Scientists, Analysts, and App Engineering teams to build a modern data platform that is secure, self-service, and production-grade.

• Operate and Improve Databricks and Snowflake :
  Own Databricks & Snowflake platforms lifecycle—including automation, workspace governance, job orchestration, and cost optimization.

• Design for Reliability :
  Architect resilient, scalable, and secure infrastructure across cloud environments. Drive initiatives around failover, autoscaling, chaos testing, and capacity planning.

• Advance Observability :
  Build and maintain platform-wide monitoring, alerting, and logging infrastructure using Datadog and other open tooling. Define and enforce SLOs/SLAs for critical services.

• Drive CI/CD for Data & ML :
  Automate deployments of data pipelines, ML workflows, and infra components using Git Hub Actions , Terraform, and related IaC tooling.

• Enable Data Flow Across Platforms :
  Build patterns and tooling to support inter- and intra-cloud data movement across systems like Snowflake, S3, Delta Lake, and Kafka.

• Champion Event-Driven Architectures :
  Leverage cloud-native tools like Event Bridge , SNS/SQS, and Lambda to build loosely coupled, scalable data systems.

• Collaborate Across Teams :
  Serve as the SRE and platform partner for teams across the organization, ensuring the platform meets the needs of analytics, data science, and product use cases.

• Contribute to Strategy :
  Influence engineering-wide decisions on data platform architecture ,
  ML enablement , and data product strategy .

• 6+ years in SRE, platform engineering, or Dev Ops roles supporting data-intensive or ML-powered applications.

• AI-native working style: daily use of Claude Code, Cursor, Copilot, or equivalent, with views on how they make a team faster.

• Hands-on Databricks experience , including workspace setup, cluster/job management, and integration with CI/CD and data orchestration tools. Experience with Snowflake as well.

• Deep understanding of cloud-native infrastructure on AWS (or similar), including VPCs, IAM, event-driven patterns, and serverless compute.

• Proven expertise with observability tools (especially Datadog) and architecting platform-wide logging and monitoring solutions.

• Strong command of CI/CD tooling , especially Git Hub Actions , infrastructure-as-code (Terraform), and deployment automation for data systems.

• Working knowledge in shell scripting and Python.

• Experience building and supporting highly available, fault-tolerant systems .

• Excellent communication and collaboration skills; able to work effectively across teams.

• Dev Sec Ops  mindset :
  Familiarity with implementing security best practices in IaC, CI/CD, secret management, and audit logging.

• Experience with
  ML infrastructure tooling such as MLflow, Feature Stores, and GPU workload orchestration.

• Strong experience in both Databricks and…