Senior Site Reliability Engineer

Datavant is a healthcare data collaboration platform that makes health data secure, accessible, and actionable for organizations across the healthcare ecosystem. The Senior Site Reliability Engineer will build and operate a resilient, observable, and scalable Data & ML Platform, including Databricks and Snowflake infrastructure, cloud systems, CI/CD automation, observability, and cross-platform data movement. The role also partners with engineering and data teams and contributes to data platform, ML enablement, and data product strategy.

Responsibilities:

• 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 GitHub 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 EventBridge, 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

Requirements:

• • 6+ years in SRE, platform engineering, or DevOps 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 GitHub 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
• This job is not eligible for employment sponsorship
• • DevSecOps 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 Snowflake in a large scale production lakehouse with cross-warehouse interoperability, e.g. Iceberg v3, Glue, etc
• • Background in compliance-aware architecture (e.g., HIPAA, SOC 2) or regulated industries
• • Familiarity with multi-cloud or hybrid cloud data environments; experience with Azure
• • Contributions to open-source infrastructure, SRE, or observability tools