Databricks Solution Architect

• 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 Responsibilities • Design 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 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 Spark • Proficiency 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 Show more Show less

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 Spark; Proficiency 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.

Design 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.