Data Engineer – Databricks Specialist

Job Summary

We are seeking a skilled Data Engineer with 3 to 5 years of experience in building scalable data pipelines and solutions, with strong hands-on expertise in Databricks. The ideal candidate should be proficient in working with large-scale data processing frameworks and have a solid understanding of Delta Lake, PySpark, and cloud-based data platforms.

Key Responsibilities:

• Design, build, and maintain robust ETL/ELT pipelines using Databricks (PySpark/SQL).
• Develop and optimize data workflows and pipelines on Delta Lake and Databricks Lakehouse architecture.
• Integrate data from multiple sources, ensuring data quality, reliability, and performance.
• Collaborate with data scientists, analysts, and business stakeholders to translate requirements into scalable data solutions.
• Monitor and troubleshoot production data pipelines; ensure performance and cost optimization.
• Work with DevOps teams for CI/CD integration and automation of Databricks jobs and notebooks.
• Maintain metadata, documentation, and versioning for data pipelines and assets.

Required Skills:

• 3–4 years of experience in data engineering or big data development.
• Strong hands-on experience with Databricks (Notebook, Jobs, Workflows).
• Proficiency in PySpark, Spark SQL, and Delta Lake.
• Experience working with Azure or AWS (preferably Azure Data Lake, Blob Storage, Synapse, etc.).
• Strong SQL skills for data manipulation and analysis.
• Familiarity with Git, CI/CD pipelines, and job orchestration tools (e.g., Airflow, Databricks Workflows).
• Understanding of data modeling, data warehousing, and data governance best practices.

Required Skills:

• 3–5 years of experience in data engineering or big data development.
• Strong hands-on experience with Databricks (Notebook, Jobs, Workflows).
• Proficiency in PySpark, Spark SQL, and Delta Lake.
• Experience working with Azure or AWS (preferably Azure Data Lake, Blob Storage, Synapse, etc.).
• Strong SQL skills for data manipulation and analysis.
• Familiarity with Git, CI/CD pipelines, and job orchestration tools (e.g., Airflow, Databricks Workflows).
• Understanding of data modeling, data warehousing, and data governance best practices.