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Mid-level, metro role with sought Databricks/GenAI skills, specialized requirements moderate candidate density.
Databricks and AWS data engineering skills are transferable, though GenAI specialization raises domain-specificity.
5+ years plus specific Databricks, AWS, and GenAI tech requirements increases shortlisting strictness.
Design, develop, and optimize scalable data pipelines and Lakehouse architectures using AWS and Databricks technologies.
Implement and manage data governance including data quality, lineage, metadata, and security across enterprise data solutions.
Develop and deploy GenAI solutions such as RAG-based chatbots, AI agents, and semantic search frameworks to enhance AI outcomes.
Minimum 5 years of relevant experience in data engineering and AWS/Databricks ecosystem.
Proficiency in AWS services including S3, Glue, Athena, Redshift, Lake Formation, Lambda, DataZone, DynamoDB.
Strong programming skills in Python, PySpark, and SQL for ETL/ELT development.
Experience with Databricks features such as Delta Lake, Iceberg, Unity Catalog, Delta Live Tables, MLflow, and AI integrations.
Experienced in designing and managing scalable and optimized Lakehouse architectures with a focus on data governance and security.
Capable of integrating GenAI solutions within enterprise data platforms leveraging AWS Bedrock, SageMaker, and LLM orchestration.
Collaborative with cross-functional teams including architects, data scientists, and stakeholders to deliver complex data solutions in an agile environment.