Databricks Data Engineer

You have 5+ years of deep, hands-on experience building production lakehouses on Databricks. You write clean PySpark and Python, model data thoughtfully, and know how to build for a multi-tenant SaaS environment. Deep production experience across the Databricks platform including Unity Catalog, Delta Live Tables, Databricks SQL, and Workflows Delta Lake as a production table format — ACID transactions, schema evolution, performance optimization, and multi-tenant governance via Unity Catalog Experience building and maintaining dbt transformation projects using the Databricks adapter in a production environment PySpark for large-scale data transformation and batch pipeline authoring Strong understanding of batch ingestion pipeline design — migrating from relational sources like MySQL and PostgreSQL into a lakehouse architecture Experience with a modern pipeline orchestrator such as Dagster, Prefect, or Databricks Workflows; Dagster experience is a strong positive Familiarity with vector databases, embedding pipelines, and RAG patterns for AI workloads — using tools such as Databricks Vector Search, pgvector, or Amazon OpenSearch Exposure to AI agent and LLM-serving infrastructure including Amazon Bedrock, AgentCore, and Strands Experience with data cataloging and governance tools such as Unity Catalog or OpenMetadata Data modeling for multi-tenant analytical workloads — partitioning strategy, schema design, and tenant isolation patterns Databricks on AWS — workspace configuration, S3 integration, IAM, and cost governance Infrastructure as code using Databricks Asset Bundles or Terraform Strong Python and SQL skills