- 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
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Design and implement scalable data pipelines using Delta Live Tables (DLT), Spark SQL, Python, or Scala.
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Optimize ETL, streaming, and ML workloads for performance, cost efficiency, and reliability.
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Administer and configure Databricks Workspaces, Unity Catalog, and cluster policies for secure, governed environments.
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Automate infrastructure and deployments using Terraform, Git, and CI/CD pipelines.
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Implement observability, cost optimization, and monitoring frameworks using tools like Splunk, Prometheus, or CloudWatch.
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Collaborate with customers to build AI and LLM solutions leveraging MLflow, DBRX, and Mosaic AI.
Requirements
Required Skills & Experience
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Strong hands-on experience with Databricks, including workspace setup, notebooks, clusters, and job orchestration.
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Expertise in Delta Lake, DLT, Unity Catalog, and SQL Warehouses.
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Proficiency in Python or Scala for data engineering and ML workflows.
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Strong understanding of AWS, Azure, or GCP cloud ecosystems.
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Experience with Terraform automation, DevOps, and MLOps practices.
Familiarity with monitoring and governance frameworks for large-scale data platforms.
