We are building a next-generation Data & AI Platform to accelerate the development and deployment of data products, analytics solutions, and ML-driven applications across Pandora. This platform will serve teams working in areas such as consumer engagement, supply chain, finance, merchandising, and manufacturing. The goal is to streamline data workflows, ensure secure and automated infrastructure provisioning, and enable fast and reliable delivery pipelines.

After re-evaluating our needs, we are now focusing on bringing in experienced DevOps engineers who can act as internal experts to help bridge gaps in areas such as networking, infrastructure automation, and cloud-native tooling. While this is not a team lead position, we expect the selected engineer to play a pivotal role in coaching peers, shaping best practices, and contributing hands-on to the platform's evolution. Exposure to ML environments is a plus, as the platform supports both traditional and advanced analytics workloads.

• 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).

• 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:

  • Experience 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.