GCP Data Engineer - ETL, SQL, Python

Astra-North Infoteck Inc. ~ Conquering today’s challenges, achieving tomorrow’s vision!; Kitchener; Hybrid; CAD 90,000 - 130,000; Full time

Job summary

Astra-North Infoteck Inc. in Kitchener, Canada, seeks a GCP Data Engineer to design and operate data pipelines on Google Cloud. The role is hybrid, with 2 days in the office weekly.

You will develop ETL/ELT workflows, optimize BigQuery datasets, and ensure data quality while collaborating with data scientists, architects, and business teams to deliver end-to-end data solutions.

Qualifications

• Experience designing and building data pipelines on cloud platforms.
• Strong SQL and data modeling skills.

Responsibilities

• Design, build, and optimize scalable data pipelines on GCP.
• Develop ETL/ELT workflows using Cloud Composer, Dataflow, Dataproc, and BigQuery.
• Implement data ingestion frameworks for batch and streaming data using Pub/Sub, Kafka, and Dataflow.
• Model, partition, and optimize BigQuery datasets to support analytics use cases.
• Collaborate with data scientists, architects, and business teams to deliver end-to-end data solutions.
• Ensure data quality, reliability, and robustness through monitoring, validation, and automation processes.
• Implement CI/CD pipelines for data workflows using Cloud Build, Git, and Terraform.
• Optimize cost, performance, and scalability across GCP data services.
• Applied security best practices, IAM policies, and compliance standards across data platforms.

Skills

ETL

SQL

Big Data

Python

Job description

Job Description: Role: GCP Data Engineer - ETL, SQL, Python

Kitchener - Hybrid: 2 days a week in office

Primary Skills

• ETL
• SQL
• Big Data
• Python

• Designed and built scalable, secure, and high performance data pipelines on Google Cloud Platform (GCP).
• Developed and optimized ETL/ELT workflows using Cloud Composer, Dataflow, Dataproc, and BigQuery.
• Implemented data ingestion frameworks for batch and streaming data using Pub/Sub, Kafka, and Dataflow.
• Modeled, partitioned, and optimized BigQuery datasets to support analytics use cases.
• Collaborated with data scientists, architects, and business teams to deliver end-to-end data solutions.
• Ensured data quality, reliability, and robustness through monitoring, validation, and automation processes.
• Implemented CI/CD pipelines for data workflows using Cloud Build, Git, and Terraform.
• Optimized cost, performance, and scalability across GCP data services.
• Applied security best practices, IAM policies, and compliance standards across data platforms.