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Metro location and known analytics employer but Snowflake specialization reduces applicant density.
Snowflake and data-warehouse expertise transfer to other data engineering roles but remain moderately specialized.
Role requires specific Snowflake, DBT, and ELT expertise, so technical filters are moderately strict.
Design, develop, and maintain Snowflake data models following the Layered Snowflake Architecture (LSA).
Build scalable ETL/ELT pipelines to ingest data from AWS S3, REST APIs, and other sources into Snowflake.
Manage Snowflake databases and implement data quality, governance, security policies, and performance optimization.
Strong expertise in Snowflake architecture, data modelling (Snowflake LSA), and SQL.
Experience in building ETL/ELT pipelines and managing Snowflake environments including tasks, streams, and stored procedures.
Professional experience in data engineering and analytics engineering with large enterprise datasets.
Work Experience Required: Not explicitly mentioned in the JD; Immediate to 30 days joiner preferred.
Experienced with enterprise-scale Snowflake data environments and complex data integration scenarios.
Familiar with AWS ecosystem and handling structured and semi-structured data formats such as JSON.
Skilled in implementing data governance, security including row-level security, performance tuning, and migration from Azure SQL to Snowflake.