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High competition due to mid-level generalist Data Engineer title, broad Snowflake skill demands, and common applicant pool.
Medium — core data engineering skills are transferable, but Snowflake specialization moderately limits cross-industry fit.
High due to explicit 5+ years and mandatory 3+ years Snowflake experience and specialized technical requirements.
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Own development and optimization of Snowflake data warehouse solutions, including schema design, query and warehouse performance tuning.
Support enterprise-scale data engineering initiatives focusing on analytics and AI-driven data products.
Implement and maintain data quality frameworks such as data validation, monitoring, and root cause analysis.
Minimum 5 years of data engineering experience.
At least 3 years of hands-on Snowflake development experience.
Expert SQL skills including complex queries and performance tuning on large datasets (hundreds of millions of records).
Notice period up to 15 days considered.
Experienced in Snowflake architecture features: dynamic tables, materialized views, streams, tasks, secure views.
Skilled in dimensional modeling and semantic layer creation for standardized KPIs and metadata management.
Familiarity with AI-powered analytical applications and emerging AI technologies like knowledge graphs, LLMs, and agent-based systems.