Databricks Data Engineer

Roles & Responsibilities

• We’re seeking a hands-on experience in Databricks with deep technical expertise in building and optimizing Lakehouse-based data and AI solutions.
• In this role, you’ll design, develop, and operationalize Delta Lakehouse architectures using Databricks, driving real-world outcomes for enterprise customers. You’ll take ownership of implementation tasks, lead technical delivery, and mentor engineering teams in best practices across data engineering, governance, and AI.

Key Responsibilities

• Design and implement scalable data pipelines using Delta Live Tables (DLT), Spark SQL, Python, or Scala.
• Optimize ETL, streaming, and ML workloads for performance, cost efficiency, and reliability.
• Administer and configure Databricks Workspaces, Unity Catalog, and cluster policies for secure, governed environments.
• Automate infrastructure and deployments using Terraform, Git, and CI/CD pipelines.
• Implement observability, cost optimization, and monitoring frameworks using tools like Splunk, Prometheus, or CloudWatch.
• Collaborate with customers to build AI and LLM solutions leveraging MLflow, DBRX, and Mosaic AI.

Required Skills & Experience

• 6 to10 Years for Strong hands-on experience with Databricks, including workspace setup, notebooks, clusters, and job orchestration.
• Expertise in Delta Lake, DLT, Unity Catalog, and SQL Warehouses.
• Proficiency in Python or Scala for data engineering and ML workflows.
• Strong understanding of AWS, Azure, or GCP cloud ecosystems.
• Experience with Terraform automation, DevOps, and MLOps practices.

Familiarity with monitoring and governanceframeworks for large-scale data platforms.

Tell employers what skills you have

Git
Monitoring Tools
Spark SQL
PySpark
Cluster
Apache Spark
Scala
Optimization of Costs
Data Pipeline
Azure DevOps
Azure Data Factory
Azure Data Lake
ETL
Databricks
Python
MLflow
System Security Policies