Requirements
Required Skills & Experience Strong 10+ hands-on experience with Databricks, including workspace setup, notebooks, clusters, and job orchestration.Expertise in Delta Lake, DLT, Unity Catalog, and SQL Warehouses.Strong 8+ Years of experience with SparkProficiency in Python or Scala for data engineering and ML workflows.Strong understanding of AWS, Azure, or GCP cloud ecosystems.Experience with Terraform automation, DevOps, and MLOps practices.Familiarity with monitoring and governance frameworks for large-scale data platforms.
Description
We’re seeking a hands-on experience in Databricks with deep technical expertise in building and optimizing Lakehouse-based data and AI solutions. In this role, you’ll design, develop, and operationalize Delta Lakehouse architectures using Databricks, driving real-world outcomes for enterprise customers. You’ll take ownership of implementation tasks, lead technical delivery, and mentor engineering teams in best practices across data engineering, governance, and AI. Key ResponsibilitiesDesign and implement scalable data pipelines using Delta Live Tables (DLT), Spark SQL, Python, or Scala.Optimize ETL, streaming, and ML workloads for performance, cost efficiency, and reliability.Administer and configure Databricks Workspaces, Unity Catalog, and cluster policies for secure, governed environments.Automate infrastructure and deployments using Terraform, Git, and CI/CD pipelines.Implement observability, cost optimization, and monitoring frameworks using tools like Splunk, Prometheus, or CloudWatch.Collaborate with customers to build AI and LLM solutions leveraging MLflow, DBRX, and Mosaic AI.