We are looking for an experienced Data Engineer with strong hands-on expertise in Databricks, PySpark, SQL, and Snowflake to design, develop, optimize, and maintain scalable data engineering solutions. The ideal candidate should have solid experience in building data pipelines, processing large datasets, and working with cloud-based data platforms.

Experience or knowledge of Databricks Genie and Snowflake CoCo is preferred. However, candidates who demonstrate solid technical fundamentals, a positive attitude, and willingness to learn these emerging capabilities will also be considered.

Key Responsibilities

Design, develop, and maintain scalable data pipelines using Databricks and PySpark.
Develop efficient and optimized SQL queries for data transformation, extraction, and analysis.
Build and manage ETL/ELT pipelines for processing structured and semi-structured data.
Work extensively with Snowflake for data ingestion, transformation, storage, and analytical workloads.
Implement data processing solutions using Apache Spark, PySpark, and Databricks.




Work with Delta Lake and implement reliable data lakehouse solutions.
Optimize Spark jobs, SQL queries, and data pipelines for performance and cost efficiency.
Develop reusable and maintainable Python/PySpark code following engineering best practices.
Troubleshoot data pipeline failures, performance issues, and production incidents.
Implement data quality, validation, monitoring, and error-handling mechanisms.
Collaborate with data architects, analysts, developers, and business stakeholders to understand requirements and deliver data solutions.
Participate in code reviews, testing, deployment, and production support activities.
Explore and adopt new Databricks and Snowflake capabilities to improve data engineering productivity.

Required Skills

4–8 years of experience in Data Engineering or a related field.
Strong hands-on experience with Databricks.
Excellent programming skills in PySpark and Python.
Strong expertise

📌 Data Bricks+Snowflake (Mumbai)
🏢 Zorba AI
📍 Mumbai