This role is for a seasoned Data Engineer Advisor with a strong background in developing and maintaining advanced data integration and transformation solutions. The position requires extensive experience with Snowflake, Data Build Tool (DBT), and Fivetran, complemented by expert SQL skills.

Ideal candidates will have a proven track record in building robust, scalable data pipelines, enforcing data quality, and supporting critical enterprise data warehousing and analytics efforts. Familiarity with CI/CD, Git-based workflows, and automated deployments is essential for modern data engineering practices.

Key responsibilities include architecting, building, and maintaining scalable ETL/ELT pipelines for data ingestion and transformation from diverse sources.

You will develop and optimize data transformation pipelines using DBT with modular practices, and create/manage data models within Snowflake across different layers. The role also involves configuring and troubleshooting data ingestion using Fivetran, alongside writing high-performance SQL queries and procedures.

Ensuring data accuracy and reliability through implementing quality checks, reconciliation, and monitoring is crucial. Collaboration with stakeholders to define requirements and deliver data solutions, optimizing Snowflake performance, and maintaining technical documentation are also core functions. This includes troubleshooting production issues, conducting root cause analysis, participating in code reviews, and supporting automated deployment processes.

We are looking for candidates with over 6 years of experience in Data Engineering, including at least 2 years specifically with Fivetran, Snowflake, and DBT.

Essential technical expertise includes strong hands-on experience with the Snowflake Data Cloud and DBT for data transformations and modeling. You should be skilled in designing and developing data pipelines for modern data lakehouse architectures (Bronze and Silver layers) and possess a deep understanding of data warehousing concepts, dimensional modeling, and data transformation patterns.

Proficiency in SQL performance optimization and Snowflake tuning techniques is required. Familiarity with CI/CD practices, Git-based development, and deployment automation is also necessary. A solid understanding of data quality, testing, monitoring, and governance frameworks is expected.