Major Job Descriptions:

• Design, implement, and maintain scalable data pipelines using Databricks Lakehouse Platform, with a strong focus on Apache Spark, Delta Lake, and Unity Catalog.
• Lead the development of batch and streaming data workflows that power analytics, machine learning, and business intelligence use cases.
• Collaborate with data scientists, architects, and business stakeholders to translate complex data requirements into robust, production-grade solutions.
• Optimize performance and cost-efficiency of Databricks clusters and jobs, leveraging tools like Photon, Auto Loader, and Job Workflows.
• Establish and enforce best practices for data quality, governance, and security within the Databricks environment.
• Mentor junior engineers and contribute to the evolution of the team’s Databricks expertise.

Job Specifications:

• Deep hands-on experience with Databricks on Azure, AWS, or GCP, including Spark (PySpark/Scala), Delta Lake, and MLflow.
• Strong programming skills in Python or Scala, and experience with CI/CD pipelines (e.g., GitHub Actions, Azure DevOps).
• Solid understanding of distributed computing, data modeling, and performance tuning in cloud-native environments.
• Familiarity with orchestration tools (e.g., Databricks Workflows, Airflow) and infrastructure-as-code (e.g., Terraform).
• A proactive mindset, strong communication skills, and a passion for building scalable, reliable data systems.
• Professional communications skills in English