Staff Engineer, Data Analytics Engineering

Design, build, and operate scalable data and AI platform capabilities across cloud-native and hybrid environments, with a primary focus on Azure and exposure to AWS, ensuring reliability, security, and operational excellence.

Responsibilities

Design, build, and operate scalable data and AI platform capabilities across cloud-native and hybrid environments, with a primary focus on Azure and exposure to AWS, ensuring reliability, security, and operational excellence; Build and maintain foundational platform services for data and analytics using Azure Databricks, ADLS Gen2, and supporting cloud services; Develop and manage Infrastructure-as-Code (IaC) and containerized platform components using Terraform, Docker, and Kubernetes fundamentals for repeatable, resilient deployments; Enable standardized, self-service platform patterns for data engineering, analytics, and AI teams; Integrate data platforms with AI platforms and model AI gateways, supporting secure, governed, and scalable AI access; Apply platform engineering and DevOps best practices through automation, CI/CD, and DataOps workflows using Python and scripting; Collaborate with platform, AI/ML, application, infrastructure, and security teams to deploy, scale, and operate platform workloads; Monitor, troubleshoot, and support production platforms in distributed, cloud-native environments, meeting reliability and SLA expectations; Contribute to platform standards, reusable patterns, documentation, and runbooks to improve self-service and developer productivity.

Requirements

Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Information Technology, or a related field; 6–8 years of experience in Data Platform Engineering or Cloud Engineering; Strong working experience with cloud-native platforms, with exposure to AWS/GCP; Experience with Azure data and analytics platforms, such as Azure Databricks and related cloud data services; Working knowledge of containerization concepts (Docker, basic Kubernetes awareness) and Linux-based environments; Hands-on experience with automation and scripting, using Python, Shell, or similar languages; Good understanding of CI/CD, DevOps, and DevSecOps principles for building secure, reliable, and scalable platforms; Familiarity with Big Data concepts and distributed data processing systems; Awareness of AI/ML and GenAI platform concepts, including AI/ML Ops and model lifecycle fundamentals; Strong problem-solving, troubleshooting, and communication skills, with the ability to collaborate effectively across teams.

Nice to have

Azure preferred