About The Role

Data Engineer – Snowflake Context Supports design and build-out of Snowflake-based data pipelines and data marts, working within an established architecture to deliver scalable, well-tested data solutions across business domains.

Key Responsibilities

Build ingestion pipelines from source systems into Snowflake, following established schema and architecture design • Develop and maintain data transformation logic using SQL/dbt/Snowflake pipelines, based on defined business and technical requirements • Build and maintain aggregation and reporting layers feeding downstream dashboards and reporting tools • Implement data lineage and audit logging at the pipeline level to support traceability and compliance • Write and maintain automated tests, including data validation checks, to catch issues early and ensure pipeline reliability • Tune Snowflake objects for performance as data volumes and domain coverage grow • Support UAT activities and resolve defects, refine logic, and iterate based on business and stakeholder feedback • Document pipelines and data objects to support maintainability and future extensibility

Required Skills & Experience

Minimum 5 years of Data and Analytics Engineering experience • Strong SQL and Python • Hands-on experience with Snowflake Tasks, Streams, Dynamic Tables, Snowpark • Snowflake Cortex (AI/ML functions within Snowflake) • dbt model development • Medallion architecture (bronze/silver/gold layering) for pipeline design • Experience building data pipelines and frameworks with strong data validation and testing practices • Strong pipeline/ELT engineering fundamentals — ingestion patterns, schema-on-write discipline, idempotent load design • Comfortable working within an established architecture and schema, executing to spec and maintaining best practices