DevOps Engineer, Data Platform

📋 Description

• • Architect and own the university’s modern data platform—designing, building, and automating every layer from ingestion to production-grade analytics and ML model deployment—so that 45,000 students and 4,500 faculty can make data-driven decisions in real time.
• • Establish rock-solid CI/CD pipelines in GitHub Actions for data engineering, analytics, and machine-learning workloads; automate testing, packaging, and zero-downtime releases that cut deployment time from days to minutes.
• • Write and maintain Infrastructure as Code (IaC) using Bicep, ARM templates, or Terraform to provision and govern Azure and Microsoft Fabric resources; version, peer-review, and continuously refactor IaC so the platform scales elastically and cost-efficiently.
• • Administer Microsoft Fabric, Azure Synapse Analytics, and related services—tuning clusters, storage, and serverless pools for high availability, sub-second query performance, and predictable spend; implement auto-scaling policies and cost-allocation tags that save the university thousands of dollars per month.
• • Build end-to-end observability: deploy Azure Monitor, Log Analytics, and custom dashboards that surface SLIs/SLOs, security anomalies, and budget alerts; create automated runbooks that self-heal common failures and page the on-call engineer only when human intervention is required.
• • Partner shoulder-to-shoulder with data scientists to containerize and deploy ML models via MLFlow, ensuring reproducible experiments, feature stores, and A/B testing environments that accelerate research and student-success initiatives.
• • Enforce enterprise-grade security and governance—configure Azure AD, RBAC, key vaults, private endpoints, and data-classification policies that satisfy FERPA and other regulatory requirements without slowing down innovation.
• • Extend the platform to Google Cloud Platform (GCP) when specialized tools or datasets require it; design secure hybrid connectivity and unified cost reporting across clouds.
• • Document everything: produce concise architecture decision records (ADRs), runbooks, and onboarding guides that empower new team members to contribute within days, not weeks.
• • Champion an automation-first culture—run internal workshops, brown-bag sessions, and code reviews that spread DevOps best practices across data scientists, analysts, and software engineers.
• • Contribute to strategic planning—translate university priorities into technical roadmaps, estimate effort, and present options that balance speed, cost, and risk.
• • Embrace continuous learning—evaluate emerging Azure services, attend virtual conferences, and prototype new tools that keep the university at the forefront of educational technology.