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Mid-level data engineer title is common, but Snowflake specialization and moderate employer brand limit competition.
Snowflake-focused data engineering skills are broadly transferable across industries and project domains.
Mandatory 5+ years plus specific Snowflake, SQL, and cloud skills increases filtering strictness.
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Design, develop, and maintain scalable data pipelines and data warehouse solutions specifically leveraging Snowflake's platform.
Develop and optimize complex SQL queries and ETL/ELT pipelines to transform, extract, and integrate data from multiple sources.
Implement and optimize Snowflake database objects (schemas, tables, views, streams, tasks) with a focus on performance, scalability, cost efficiency, and data quality validation.
5+ years of experience in data engineering or a related field.
Strong hands-on experience with Snowflake, including Snowflake SQL, stored procedures, Streams, Tasks, Stages, Snowpipe, and other Snowflake features.
Advanced SQL skills and solid understanding of data warehousing concepts: star/snowflake schemas, fact/dimension tables, SCD, data modeling, and ETL/ELT processes.
Experience with cloud platforms (AWS, Azure, or GCP) and familiarity with Git and CI/CD practices.
Demonstrates deep technical expertise in Snowflake architecture and advanced data warehousing techniques including incremental loading and change data capture.
Experienced in designing robust scalable data pipelines and optimizing them for cost and performance in cloud environments.
Familiar with Agile/Scrum workflows and capable of troubleshooting complex data processing and pipeline issues under production conditions.