Key Responsibilities
• Design scalable data, analytics, machine learning, and GenAI solutions using the Databricks Lakehouse
Platform.
• Provide architecture guidance for Databricks workspaces, clusters/serverless compute, workflows, Delta
Lake, and Unity Catalog.
• Build and optimize data pipelines using Spark, PySpark, SQL, Delta Lake, and Databricks Workflows.
• Support implementation of medallion architecture patterns across bronze, silver, and gold layers.
• Design and guide GenAI solutions such as RAG pipelines, embeddings, vector search, AI assistants, and
model serving endpoints.
• Implement governance and security patterns using Unity Catalog, including access controls, lineage,
auditability, and policy enforcement.
• Support MLOps/LLMOps practices using MLflow, model registry, model serving, monitoring, and CI/CD.
• Partner with Databricks, engineering, data science, analytics, and business teams to translate requirements
into scalable technical solutions.
Required Qualifications
• 8+ years of experience in data architecture, data engineering, analytics engineering, ML engineering, or
cloud data platforms.
• 4+ years of hands-on experience with Databricks.
• Strong knowledge of Databricks Lakehouse, Delta Lake, Unity Catalog, Databricks SQL, Workflows, and
MLflow.
• Hands-on experience with Python, PySpark, SQL, and Apache Spark.
• Experience designing and deploying production-grade data pipelines and AI/ML solutions.
• Working knowledge of cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Strong understanding of data governance, security, access control, and enterprise architecture principles.
• Excellent communication skills with the ability to work with both technical and business stakeholders.
Preferred Qualifications
• Databricks certification preferred, such as:
o Databricks Certified Data Engineer Professional
o Databricks Certified Machine Learning Professional
o Databricks Certified Generative AI Engineer Associate
• Experience with Mosaic AI, Databricks Vector Search, and Databricks Model Serving.
• Experience building RAG applications, LLM-powered assistants, AI agents, or GenAI platforms.
• Knowledge of LLM evaluation, prompt management, responsible AI, and GenAI governance.
• Experience with CI/CD, Terraform, GitHub Actions, Azure DevOps, or similar tools.
• Prior consulting, customer-facing, or resident architect experience is a plus.
Core Skills
Databricks | Delta Lake | Unity Catalog | Spark | PySpark | SQL | MLflow | Mosaic AI | Vector Search | RAG | LLMs
| Model Serving | MLOps | LLMOps | Cloud Data Platforms | Data Governance | CI/CD
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