DataOps Engineer

We are looking for a DataOps Engineer to own the infrastructure that powers Placer’s large-scale data processing platform. This is a platform-facing role sitting at the intersection of data engineering and infrastructure — you’ll be the person who makes Spark run reliably and efficiently on Kubernetes, so that data engineers can build with confidence.

Requirements

• Design, deploy, and operate the Kubernetes-based infrastructure that runs Apache Spark and large-scale data processing workloads
• Own the reliability, performance, and cost-efficiency of the data platform — including SLAs, autoscaling, resource quotas, and workload isolation
• Manage Spark-on-K8s configurations, Airflow infrastructure, and Databricks integration; tune for throughput, latency, and cost
• Build and maintain CI/CD pipelines and infrastructure-as-code for data platform components
• Develop observability tooling — metrics, logging, alerting, and data quality dashboards — to proactively surface issues across the pipeline stack
• Collaborate closely with Data Engineers to understand workload patterns and translate them into infrastructure decisions
• Manage cloud storage (GCS/S3), Delta Lake, and Unity Catalog infrastructure
• Drive platform improvements end-to-end: from design through deployment and ongoing ownership

Benefits

• Competitive salary
• Excellent benefits