APIs. Proficiency with Kafka and distributed streaming systems. Solid understanding of SQL and data modeling. Experience with containerization (Docker) and orchestration (Kubernetes). Working knowledge of Flink, Spark, or Databricks for data processing. Familiarity with AWS services (ECS, EKS, S3, Lambda, etc.). Basic scripting in Python for automation or data manipulation. Secondary Skills Experience with Datadog, Prometheus, or other More ❯
control, policies, and directory service integration. Architect the networking, storage, monitoring, and logging layers required to support a high-volume, data-intensive platform. Prepare the foundational components to support Databricks as the target lakehouse environment. Produce clear, auditable documentation covering architecture decisions, deployment patterns, and reusable templates. Collaborate closely with strategy and architecture stakeholders to ensure alignment with data governance … enterprise-scale Azure environments, ideally within security-conscious or regulated domains. Deep, hands-on expertise in Azure governance, security, IAM, and automation (Terraform, Bicep etc). Strong understanding of Databricks and related lakehouse concepts. Comfortable operating in greenfield or rapidly evolving environments, defining standards where few yet exist. Able to produce high-quality builds and concise, well-structured technical documentation. More ❯
