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Specialized Snowflake/Databricks lead role in metros narrows supply but data roles remain moderately competitive.
Platform-specific Snowflake/Databricks expertise limits portability despite broadly transferable data engineering skills.
Mandatory 7–10 years, hands-on Snowflake or Databricks experience and leadership make shortlisting highly stringent.
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Design, develop, and optimize scalable data solutions primarily on Snowflake and/or Databricks platforms.
Build and maintain robust, high-performance ETL/ELT data pipelines ensuring data quality, reliability, and performance.
Provide technical leadership by mentoring engineers, conducting code reviews, and collaborating with cross-functional teams to deliver scalable data engineering solutions.
7–10 years of experience in data engineering with strong hands-on expertise in at least one platform: Snowflake or Databricks.
Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Engineering, or related field.
Proficiency in SQL and Python; Spark experience mandatory for Databricks-focused roles.
Experience building data pipelines on cloud platforms such as AWS, Azure, or GCP.
Demonstrated leadership in guiding and mentoring data engineering teams and managing technical projects.
Deep practical experience with Snowflake data modeling, performance tuning, or Databricks Spark processing and Delta Lake concepts.
Operates effectively in cloud environments with expertise in data orchestration tools like Apache Airflow or Azure Data Factory and CI/CD infrastructure practices.