GCP Data Engineer

Role Name: GCP Data Engineer Work Location: Toronto, Canada (Remote) Job Summary We are seeking an experienced GCP Data Engineer with hands-on expertise in Google Cloud Platform (GCP), particularly BigQuery, and familiarity with DBT for data transformation and modeling. The ideal candidate will have strong SQL skills, a solid understanding of data warehousing concepts, and basic to intermediate Python skills for scripting and automation. Experience working with large-scale operational and customer interaction datasets is preferred. Key Responsibilities • Design, build, and maintain scalable data pipelines using GCP services, primarily BigQuery. • Develop and manage DBT models to transform raw data into clean, tested, and documented datasets. • Write complex, optimized SQL queries for data extraction, transformation, and analysis. • Implement and maintain data warehousing solutions, ensuring performance, scalability, and reliability. • Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements and deliver high-quality data solutions. • Monitor and troubleshoot data pipeline performance and data quality issues. • Automate data workflows and tasks using Python scripts where required. • Follow data governance, security, and compliance best practices. Required Qualifications • Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent experience. • At least 5 years of experience in data engineering or a similar role. • Strong SQL skills, including the ability to write complex and efficient queries. • Hands-on experience with BigQuery and other GCP data services. • Familiarity with DBT for data transformation and modeling. • Solid understanding of data warehousing principles and best practices. • Basic to intermediate Python skills for scripting and automation. • Familiarity with version control systems such as Git. • Strong problem-solving, analytical, and communication skills. Preferred Qualifications • Experience working with large-scale operational, customer interaction, or network-related datasets. • Exposure to data transformation, modeling, and workflow automation best practices.