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Metro location and popular data-engineer title increase competition, but Snowflake specialization reduces density.
Snowflake and PySpark skills are industry-transferable but require data-platform experience, so moderate cross-industry fit.
Multiple mandatory technical skills (Snowflake, SQL, PySpark) and senior mentoring responsibilities imply strict technical filters.
Design, develop, and optimize ETL/ELT pipelines using Snowflake, SQL, and PySpark to support analytics and reporting.
Write and tune complex SQL queries and stored procedures; manage Snowflake environment including warehouses, schemas, roles, and access controls.
Use AI-assisted tools to accelerate coding and debugging; mentor junior engineers and establish best practices.
Strong proficiency in SQL including query optimization and performance tuning.
Solid experience with Python and PySpark for large-scale data processing.
Experience in Snowflake development including SnowSQL, Snowpipe, Streams, Tasks, and role-based access control.
Bachelor's degree in Computer Science, IT, Engineering, or equivalent practical experience; Work Experience Required: Not explicitly mentioned in the JD.
Experienced in designing and implementing Snowflake data warehouse/lake architectures.
Able to effectively collaborate with analysts, data scientists, and engineers to translate requirements into technical solutions.
Skilled in leveraging AI/GenAI tools to enhance productivity in development and documentation.