Seeking a skilled Data Engineer Advisor with over 6 years of experience in building advanced data integration and transformation solutions. The role requires deep expertise in Snowflake, Data Build Tool (DBT), and Fivetran, complemented by strong SQL development capabilities. You will be instrumental in creating robust data pipelines, ensuring data integrity, and supporting enterprise-level data warehousing and analytics projects.

Proficiency in CI/CD practices, Git workflows, and automated deployments is essential for driving modern data engineering excellence.

Develop and maintain scalable ETL/ELT pipelines to ingest, transform, and load data from various sources. Craft and optimize data transformation pipelines using DBT, focusing on modular and reusable designs. Construct and manage data models within Snowflake across different layers. Configure, manage, and troubleshoot data ingestion processes with Fivetran. Write high-performance SQL queries and stored procedures. Implement data quality checks and monitoring to guarantee data accuracy. Collaborate with cross-functional teams to deliver effective data solutions.

Optimize Snowflake performance through tuning and partitioning strategies. Document source-to-target mappings and data lineage. Analyze and resolve production issues, conducting root cause analysis. Participate in code reviews and uphold development best practices. Support automated deployment processes across environments.

A minimum of 6 years in Data Engineering, with at least 2 years specifically using Fivetran, Snowflake, and DBT. Demonstrable hands-on expertise with the Snowflake Data Cloud. Strong experience in data transformations and modeling using DBT. Proven ability to design and develop data pipelines for data lakehouse architectures (Bronze/Silver layers). Solid understanding of data warehousing principles, dimensional modeling, and data transformation patterns.

Skilled in SQL performance tuning and Snowflake optimization techniques. Familiarity with CI/CD, Git-based development, and deployment automation. Knowledge of data quality, testing, monitoring, and governance frameworks is crucial.