Note: The job is a remote job and is open to candidates in USA. 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
Skills
- * **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
Company Overview