
Chief Data Pipeline Architect
6 hours ago
About Telus Digital AI Data Solutions
We are a global leader in providing premium data services for the ever-evolving AI ecosystem.
From machine learning to computer vision and GenAI, we empower the next generation of AI-powered experiences with high-quality data and human intelligence to test, train, and improve AI models.
We deliver solutions designed to cover the training data needs of every project. From custom data collection to advanced data annotation and fine-tuning, our purpose-built tools deliver multimodal data for AI training projects of any complexity.
Our expertise spans data engineering, ETL/ELT processes, cloud-based data solutions, and more – delivered with secure infrastructure and exceptional service.
The Role
We are looking for a hands-on, action-oriented Data Engineer who thrives on execution.
This role is perfect for someone who enjoys building robust data pipelines, delivering solutions that directly impact the business, and values outcomes over process.
Key Responsibilities
Build, deploy, and manage data pipelines using Python and PySpark
Develop and optimize ETL/ELT processes to support data integration across systems
Work directly with GCP and AWS services to implement scalable cloud-based data solutions
Own data workflows end-to-end — from ingestion to transformation to storage
Continuously monitor and improve pipeline reliability, speed, and data quality
Use GenAI and automation tools to speed up development and reduce manual effort
Proactively debug, troubleshoot, and resolve data engineering issues
Ensure data is available and trustworthy for analytics and downstream systems
Deliver high-quality code and documentation with a bias for action
Requirements
Strong proficiency in Python and PySpark
Working experience with cloud platforms like GCP and/or AWS
Solid understanding of ETL/ELT, data warehouse and data lake concepts.
Proficient in working with relational databases—preferably PostgreSQL and MySQL—as well as non-relational databases, with a focus on MongoDB.
Driven by delivery and results — you get things done efficiently
Self-starter attitude with minimal need for hand-holding
Excitement for automating work using GenAI or scripting tools
Familiarity with SCD, CDC, Real-time Streaming vs Batch-processing.
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