Databricks Engineer

Role

Position

Type

Duration

Role Overview

We are seeking a Mid-Level Databricks Engineer with strong data engineering fundamentals and hands-on experience building scalable data pipelines on the Databricks platform. The ideal candidate will be comfortable working with distributed data processing, designing Delta Lake architectures, implementing CI/CD for data workloads, and optimizing Spark jobs for performance and reliability.

Required Skills

• Strong hands-on experience in building data pipeline using scripting language includes Python, PySpark and SQL
• Strong working experience with Databricks (Unity Catalog is plus) and Azure Data Factory (ADF)
• Hands-on working experience with Azure DevOps Repo and CI/CD
• Strong understanding of modern Data Architecture, Data Warehousing concepts, Data Modelling and ETL/ELT processes
• Understanding of Agile ways of working
• Any experience working in DBT is a plus
• Working with Retail Domain is a plus

Platform Engineering & Operations

• Configure and optimize Databricks clusters, including autoscaling, cluster sizing, and runtime selection.
• Manage databases, schemas, and Delta Lake tables, including vacuuming, Z-ordering, partitioning, and optimization.
• Work with catalogs, Unity Catalog (UC), and access controls for secure data governance.
• Monitor and troubleshoot performance issues across jobs, clusters, and workflows.

CI/CD & DevOps

• Implement CI/CD pipelines for Databricks workflows using GitHub Actions / Azure DevOps / GitLab.
• Work with Databricks Repos and Git workflows (branching, PRs, code reviews).
• Version and promote data pipelines across environments.

Cloud & Integration

• Integrate Databricks with cloud services such as:
• Azure: ADLS Gen2, Key Vault, ADF, Event Hub, Synapse
• AWS: S3, Glue, IAM, Lambda, Kinesis (depending on cloud environment)
• Work with data ingestion frameworks, messaging systems, and event-driven architectures.

Quality, Testing & Documentation

• Implement unit tests and integration tests using pytest, SQL tests, or dbx testing frameworks.
• Maintain pipeline documentation, data dictionaries, and technical design artifacts.
• Ensure high data quality, reliability, and observability.