Staff Engineer, Data Analytics Engineering

Key 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.

Required Qualifications:

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 Azure preferred and 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.

Sandisk is committed to providing equal opportunities to all applicants and employees and will not discriminate based on their race, color, ancestry, religion (including religious dress and grooming standards), sex (including pregnancy, childbirth or related medical conditions, breastfeeding or related medical conditions), gender (including a person’s gender identity, gender expression, and gender-related appearance and behavior, whether or not stereotypically associated with the person’s assigned sex at birth), age, national origin, sexual orientation, medical condition, marital status (including domestic part