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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Site Reliability Engineer - **Company:** Datavant - **Location:** Salem, OR, United States - **Experience:** Expert - **Salary:** $168,000.0 - $200,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Audit Trail, Microsoft Azure, Cloud Computing, Continuous Integration, Data Infrastructure, Data Systems, Cursor (Graphical User Interface Elements), DevOps, Fault Tolerance, Github, Identity and Access Management, Python (Programming Language), Open Source Technology, Reliability Engineering, Azure Machine Learning, Shell Script, Data Streaming, Datadog, Data Logging, Cloud Platform System, Autoscaling, Snowflake, Grafana, Multi-Cloud, HybridCloud, Event Driven Architecture, Data Lakes, Deployment Automation, Apache Kafka, Machine Learning Operations, Amazon Simple Queue Service (SQS), Terraform, Data Pipelines, Devsecops, Serverless Computing, Databricks - **Published:** August 14, 2026 - **Apply:** https://dejobs.org/x/x/7ED16DE5B7D24526AA9430DFA476B3AE/job/ ## About the Role * 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. What Helps You Stand Out * 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. ## Description 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. What You Will Do * 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 . ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Debugging in the Dark](https://www.wearedevelopers.com/videos/1658-debugging-in-the-dark) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Software Engineering Social Connection: Yubo’s lean approach to scaling an 80M-user infrastructure](https://www.wearedevelopers.com/videos/1583-software-engineering-social-connection-yubo-s-lean-approach-to-scaling-an-80m-user-infrastructure) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Is Software Engineering Over-Saturated?](https://www.wearedevelopers.com/magazine/418-is-software-engineering-over-saturated) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk)