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Tier-1 brand, metro location, and generalist data-engineer title yield high applicant competition.
Finance analytics context and Snowflake expertise moderately restrict cross-industry transferability.
Explicit 8+ years expectation plus mandatory Snowflake, ETL, and Python expertise raise strictness.
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Design and maintain Snowflake dimensional data models and optimize SQL transformations to support analytics and reporting.
Develop and maintain ELT/ETL data pipelines, including implementing data quality, validation, and reconciliation controls.
Manage Snowflake environments and improve performance using query profiling, warehouse sizing, clustering, and leverage AI tools like Snowflake AI features for enhanced delivery.
5-10 years of professional experience specifically with Snowflake and related technologies.
Expertise in SQL, data modeling, ELT/ETL pipelines with knowledge of Snowflake advanced features like AI/ML integration and materialized views.
Proficiency in Python for data processing including tools like Pandas and NumPy; knowledge of GenAI, LLMs, and AI tools used in workflows (e.g., GitHub Copilot, Snowflake CoCo).
Work Experience Required: At least 8 years of relevant experience generally expected; Notice Period: Not explicitly mentioned in the JD.
Experienced in managing and optimizing large-scale Snowflake data environments in a financial services context.
Strong technical skill set combining deep SQL/data modeling expertise with practical AI/ML and modern data engineering tools.
Capable of operating independently and collaboratively in a fast-paced, global finance technology team focused on sustaining and growing a revenue-generating analytics platform.