Job Description

Key Responsibilities
Design, develop, and maintain metadata-driven data pipelines using ADF and Databricks.
Build and implement end-to-end metadata frameworks, ensuring scalability and reusability.
Optimize data workflows leveraging SparkSQL and Pandas for large-scale data processing.
Collaborate with cross-functional teams to integrate data solutions into enterprise architecture.
Implement CI/CD pipelines for automated deployment and testing of data solutions.
Ensure data quality, governance, and compliance with organizational standards.
Provide technical leadership and take complete ownership of assigned projects.
Technical Skills Required
Azure Data Factory (ADF): Expertise in building and orchestrating data pipelines.
Databricks: Hands-on experience with notebooks, clusters, and job scheduling.
Pandas: Advanced data manipulation and transformation skills.
SparkSQL: Strong knowledge of distributed data processing and query optimization.
CI/CD: Experience with tools like Azure DevOps, Git, or similar for automated deployments.
Metadata-driven architecture: Proven experience in designing and implementing metadata frameworks.
Programming: Proficiency in Python and/or Scala for data engineering tasks
Good understanding of DevOps process/automation

• Databricks asset bundles
• Azure DevOps

• Python understanding is good considering the fact he was able to answer questions related to python pkg wheel etc.. [usually, data engineers find it difficult to answer]
• Good understanding of observability
• Good understanding of pyspark, scenario-based questions -> watermark management, data protection aspects handling etc.
• Unable to answer question related to server less clusters

Skills Required