Cloud Engineer

Required

• 5–8 years of experience in cloud engineering, DevOps, or a software engineering role with significant infrastructure ownership.
• Bachelor's degree in Computer Science, Engineering, or a related field or equivalent work experience.
• Strong proficiency in Python; experience with at least one additional language (TypeScript/Node.js, Go, or equivalent).
• Deep hands-on experience with Azure cloud services, including compute, networking, storage, identity, and managed services; familiarity with Azure CAF landing zones, subscription governance, and resource management at scale.
• Proficiency with Terraform, including module development, remote state management, and PR-based workflow; Bicep familiarity a plus.
• Experience designing and implementing CI/CD pipelines, preferably using GitHub Actions.
• Production experience with Kubernetes / AKS — deploys, scaling, health management, upgrades, and cluster operations.
• Solid Docker and container image management skills; experience building and maintaining containerized services in production.
• Azure networking and security fundamentals, including Private Link, network rules, NSGs, and RBAC; comfort managing secrets hygiene across environments.
• Azure cost management and FinOps awareness: budget alerts, cost/cluster policies, anomaly detection and response.
• Comfort operating in and improving existing codebases with limited live handoff — able to orient independently, read unfamiliar infrastructure, and contribute quickly without extensive documentation.
• Experience with Databricks or equivalent large-scale data processing platforms.
• Solid understanding of data security principles, IAM patterns, and compliance frameworks (SOC 2, HIPAA, ISO 27001, or equivalent).
• Experience operating a self-hosted observability stack — specifically Grafana, Loki, and Prometheus — including patching, upgrades, and dashboard maintenance; equivalent stack experience considered.
• Ability to work independently on complex, ambiguous problems and communicate technical decisions clearly to non-technical stakeholders.
• Strong written communication skills; comfortable producing architecture documentation, runbooks, and technical specifications.

Requirements

A combination of relevant work experience and education equivalent to 5–8 years of hands-on cloud engineering or software engineering experience, with a demonstrable record of owning and delivering complex infrastructure and software projects. A bachelor's degree in Computer Science, Engineering, or a related field is preferred but not required — equivalent professional experience will be considered.

Experience Level

Mid-Senior Level

#Ll-DP1

• Experience supporting research computing or academic data infrastructure environments.
• Familiarity with ML infrastructure tooling (MLflow, Hugging Face Hub, model serving frameworks).
• Experience with IRB-compliant research data environments or sensitive data handling at scale.
• Frontend development experience (React or equivalent) — useful on a small cross-functional team.
• Relevant certifications: Azure Administrator (AZ-104), Azure Solutions Architect (AZ-305), Azure DevOps Engineer (AZ-400), or equivalent.

• Design, deploy, and maintain cloud infrastructure on Azure, with responsibility for performance, cost-effectiveness, and reliability across research and production environments.
• Architect and manage Databricks workspaces, including compute cluster configuration, access controls, and cost optimization for large-scale data processing workflows.
• Manage Azure networking, storage, identity (Azure AD / Entra ID), and resource governance across multiple environments.
• Implement infrastructure-as-code using Terraform and/or Bicep; maintain version-controlled, reproducible infrastructure definitions including modules, remote state management, and PR-based workflow.
• Deploy, operate, and maintain AKS clusters running containerized workloads — including containerized data crawlers — managing deploys, scaling, health monitoring, patching, and upgrades.
• Administer Azure Blob Storage, including lifecycle policies, redundancy configuration, and access tier management.
• Manage Azure networking and security, including Private Link, network rules, RBAC, and secrets hygiene across environments.
• Own Azure cost management: budget alerts, cost/cluster policies, anomaly detection and response, and FinOps practices to keep infrastructure spend predictable and efficient.

• Design, build, and maintain backend services, APIs, and data pipelines using Python and/or TypeScript/Node.js.
• Develop and maintain CI/CD pipelines using GitHub Actions, ensuring reliable and automated delivery of infrastructure and application changes.
• Build and maintain internal tooling that improves the experience and efficiency of the research and operations teams.
• Contribute to frontend integrations where needed; comfortable working across the stack on a small team.

• Develop and support data pipelines for ingesting, transforming, and serving large-scale behavioral and social media datasets to researchers.
• Implement and maintain infrastructure for machine learning workflows, including model serving, experiment tracking, and compute resource management.
• Support integration with ML frameworks and tools (e.g., MLflow, Hugging Face, or equivalent) within the managed environment.

• Implement and maintain security controls across all systems, including encryption at rest and in transit, identity and access management, network segmentation, and secrets management.
• Design and operate environments meeting IRB, data governance, and institutional compliance requirements; ensure adherence to standards equivalent to SOC 2, HIPAA, or ISO 27001 as applicable.
• Conduct regular security reviews, vulnerability assessments, and penetration test coordination; manage remediation tracking.
• Implement audit logging, access controls, and data handling procedures for sensitive research data in compliance with IRB protocols and data use agreements.

• Operate, patch, and upgrade the self-hosted observability stack — Grafana (dashboards), Loki (log aggregation), and Prometheus (metrics) — including security patching and version upgrades; implement and maintain alerting, distributed tracing, and platform-wide monitoring.
• Own incident response, root cause analysis, and operational reliability for production systems.
• Develop and maintain runbooks, architecture documentation, and operational procedures.

Benefits Eligible

Yes

Probationary Period 180 days

Essential Services Personnel (see policy for detail) No

Estimated Appointment End Date 9/30/2027

Physical Capacity Exam Required No

Valid Driver’s License Required No

Salary Range

$130,000 to $140,000

The Accelerator at Princeton's School of Public and International Affairs (SPIA) builds shared infrastructure for large-scale, multi-institutional research on the information environment. Our platform — the Accelerator Cloud Environment (ACE) — runs on Azure with AKS-hosted data crawlers, Blob Storage, and a Databricks pipeline, with infrastructure managed as code in Terraform and CI/CD in GitHub Actions. We serve a global consortium of academic researchers and currently support 11 active research projects across 7 institutions.

We are a small team that moves quickly, values technical ownership, and cares deeply about the quality and security of the systems we build.

The University considers factors such as (but not limited to) scope and responsibilities of the position, candidate's qualifications, work experience, education/training, key skills, market, collective bargaining agreements as applicable, and organizational considerations when extending an offer. The posted salary range represents the University's good faith and reasonable estimate for a full-time position; salaries for part-time positions are pro-rated accordingly.

If the salary range on the posted position shows an hourly rate, this is the baseline; the actual hourly rate may be higher, depending on the position and factors listed above.

The University also offers a comprehensive benefit program to eligible employees. Please see this link for more information.

Standard Weekly Hours 20.00

Eligible for Overtime No