GCP Data Engineer
Querentia
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.
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Develop and manage DBT models to transform raw data into clean, tested, and documented datasets.
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Write complex, optimized SQL queries for data extraction, transformation, and analysis.
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Implement and maintain data warehousing solutions, ensuring performance, scalability, and reliability.
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Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements and deliver high-quality data solutions.
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Monitor and troubleshoot data pipeline performance and data quality issues.
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Automate data workflows and tasks using Python scripts where required.
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Follow data governance, security, and compliance best practices.
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Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent experience.
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At least 5 years of experience in data engineering or a similar role.
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Strong SQL skills, including the ability to write complex and efficient queries.
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Hands-on experience with BigQuery and other GCP data services.
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Familiarity with DBT for data transformation and modeling.
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Solid understanding of data warehousing principles and best practices.
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Basic to intermediate Python skills for scripting and automation.
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Familiarity with version control systems such as Git.
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Strong problem-solving, analytical, and communication skills.
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Experience working with large-scale operational, customer interaction, or network-related datasets.
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Exposure to data transformation, modeling, and workflow automation best practices.
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