We are looking for a highly skilled Azure Databricks & Snowflake Data Engineer with robust experience in Spark, PySpark, Python/Scala, Azure Cloud, SQL, ETL/ELT pipelines, and Snowflake development. The ideal candidate should have hands-on experience in building and migrating enterprise data pipelines and should be comfortable working across the complete Software Development Life Cycle.

Exposure to Snowflake Cortex/CoCo, Databricks Genie, and AI-assisted coding tools such as GitHub Copilot is highly desirable. Candidates with strong core engineering skills and a willingness to learn emerging AI capabilities will also be considered.

Key Responsibilities

Design, develop, optimize, and maintain scalable data engineering solutions using Azure Databricks, Apache Spark, PySpark, Python, and/or Scala.
Develop and optimize complex ETL/ELT data pipelines for large-scale data processing.
Perform migration of existing ETL/ELT pipelines and data workloads to Azure Databricks and Snowflake.
Develop robust data transformation, ingestion, validation, and processing frameworks.




Work extensively with Snowflake for data loading, transformation, optimization, and integration.
Collaborate with data architects, developers, analysts, and business teams to understand requirements and deliver scalable solutions.
Implement CI/CD pipelines and automated deployment processes using Git, GitHub, GitHub Workflows, and Azure DevOps.
Follow software engineering best practices including version control, code reviews, unit testing, debugging, and documentation.
Work with workflow/orchestration technologies such as Azure Data Factory (ADF), Azure Synapse, and Airflow.
Participate in architecture discussions and contribute to improving data platform performance, reliability, and scalability.
Utilize Terraform and Infrastructure-as-Code concepts where applicable.
Explore and leverage AI-assisted development tools such as GitHub Copilot and Databricks Genie to improve engineering productivity.
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📌 Senior Azure Databricks (Noida)
🏢 Zorba AI
📍 Noida