GCP Analytics Engineer

Role overview Develop and improve analytical data transformation workflows built around dbt and BigQuery. The role covers data modeling, runtime and artifact lifecycle management, execution efficiency, and collaboration with data engineering and platform teams to support governed, reliable analytics layers.

Responsibilities - Design, develop, and maintain dbt transformation frameworks and analytical models. - Build and optimize models for Silver and Gold data layers in BigQuery. - Improve dbt execution speed and operational efficiency, and simplify transformation workflows. - Manage dbt artifact versioning and lifecycle; separate models from runtime Docker images and establish controlled distribution. - Assess when native BigQuery execution approaches are suitable alternatives. - Coordinate with engineering, DevOps, and platform teams on data quality, governance, and technical documentation.

Requirements - Strong commercial experience with dbt and strong SQL skills. - Hands-on BigQuery experience and a background building analytical or transformation layers on modern data platforms. - Experience developing and maintaining data models for enterprise analytics environments. - Understanding of model lifecycle management and versioning. - Experience with Docker-based deployment approaches and performance optimization for data transformations. - Familiarity with Python and strong communication and documentation skills.

Nice to have - Apache Airflow experience, including BigQuery job operators or similar orchestration patterns. - Familiarity with Google Cloud Storage and Kubernetes. - Experience creating reusable transformation frameworks and optimizing cloud processing costs. - Background in regulated sectors such as banking, or in data quality and governance initiatives.