Requirements

Operating system

Windows

Our requirements

Infrastructure & DevOps:

- Strong experience with Infrastructure as Code (IaC) tools such as Terraform.

- Deep understanding of CI/CD concepts and tools (e.g., GitHub Actions, Jenkins, ArgoCD, or similar).

- Solid Linux systems knowledge and scripting (e.g., Bash, Python).

Cloud & Platform Engineering:

- Hands-on experience with cloud platforms (preferably Azure; AWS/GCP also acceptable).

- Experience with containerization and orchestration tools (Docker, Kubernetes).

- Familiarity with cloud-native networking, VPC design, security policies, and IAM.

Programming & Automation:

- Proficiency in Python for automation and scripting tasks.

- Experience building custom integrations and developer tooling using REST APIs and SDKs.

Data & AI Platform Exposure:

- Understanding of data platform components (data lakes, lakehouses, streaming systems).

- Familiarity with platforms such as Databricks or other ML/data product infrastructure.

Observability & Reliability:

-bExperience with monitoring and alerting tools (e.g., Prometheus, Grafana, ELK, NewRelic).

- Good understanding of SLAs, SLOs, and system reliability practices.

Collaboration & Coaching:

- Strong communication skills and ability to work across cross-functional teams.

- Appetite for mentoring, sharing knowledge, and contributing to internal enablement.

Your responsibilities

• Design, build, and maintain the infrastructure and tooling for the Data & AI Platform used by multiple product teams across Pandora.
• Implement and optimize CI/CD pipelines to support rapid, safe, and scalable deployment of data and ML products.
• Develop and manage infrastructure as code (e.g., Terraform) to automate provisioning and ensure consistency across environments.
• Create and maintain internal SDKs, templates, and tooling that enable fast and secure development of data products.
• Enable seamless integration between traditional data pipelines and ML/AI workloads, including environment setup, packaging, and deployment.
• Drive adoption of DevOps best practices, observability standards, and cloud-native design across platform components.
• Collaborate closely with Data Engineers, ML Engineers, and Platform teams to scope work, identify gaps, and deliver improvements.
• Coach and mentor peers on DevOps tooling, infrastructure design, and cloud architecture.
• Contribute to the platform’s security, privacy, and cost optimization practices (FinOps).