Databricks Data 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 hands-on experience with Databricks, including workspace setup, notebooks, clusters, and job orchestration.
• Expertise in Delta Lake, DLT, Unity Catalog, and SQL Warehouses.
• 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.

Good to Have Skills:

• Machine Learning, Deep Learning, NLP, or Generative AI
• Designing distributed and scalable systems
• API-first and microservices architecture
• Python, ML frameworks (TensorFlow, PyTorch, Scikit-learn)
• MLOps tools (MLflow, Kubeflow, SageMaker, etc.)
• Data platforms (Spark, Databricks, Snowflake)