Senior Databricks Engineer

Apache Spark: Mastery of Spark SQL and the DataFrame API for distributed data processing, performance tuning, and optimizing execution plans.

Delta Lake & Lakehouse: Building medallion architectures (Bronze, Silver, Gold layers) and using optimizations like Z-Ordering and ACID transactions

Pipelines & Orchestration: Developing with Delta Live Tables (DLT) and Databricks Workflows to design resilient, idempotent ELT/ETL pipelines.

Unity Catalog: Configuring data governance, row/column-level security, and data lineage

Languages: Advanced proficiency in Python (PySpark) and SQL

Storage & Data Lakes: Expert in reading/writing to Amazon S3 and integrating with AWS data warehouses (e.g., Amazon Redshift).

Security & IAM: Implementing cross-account IAM roles, S3 bucket policies, and Customer-Managed Keys (CMK) via AWS KMS

.Compute Management: Managing cluster policies, instance profiles, and utilizing Spot Instances to optimize Databricks compute costs on AWS.

Ecosystem Integration: Familiarity with integrating Databricks jobs alongside native services like AWS Glue, Amazon Kinesis, and AWS Step Functions

CI/CD & Version Control: Git-based workflows using Databricks Git Folders and managing deployments.

AI & MLOps: Using MLflow for experiment tracking and model registries.

Familiarity with LLMs, Vector Search, and GenAI integrations.

Data Pipeline Development: Extract, transform, and load (ETL) data from multiple sources, building batch and streaming pipelines.Spark & Code Optimization: Write highly efficient PySpark, Scala, or SQL code.

Troubleshoot distributed processing bottlenecks and optimize job performance.

Lakehouse Management: Work with the Medallion architecture to transition data through Bronze (raw), Silver (cleansed), and Gold (aggregated) layers in Delta Lake.

Orchestration & Automation: Schedule and automate workflows using Databricks Jobs, Workflows, or Delta Live Tables (DLT).