Senior Data Engineer

at  TELUS Digital

The Senior Data Engineer will play a pivotal role in driving data-driven decision-making at TELUS International. This role requires a strong technical background in data engineering, coupled with a passion for building scalable and reliable data solutions. The ideal candidate will be a hands-on data engineer with expertise in data pipelines, data warehousing, and cloud technologies.

QUALIFICATIONS AND SKILLS

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• 5+ years of hands-on experience in data engineering and analysis.
• Strong proficiency in Python, SQL, and other relevant programming languages.
• Expertise in data warehousing concepts and tools (e.g., BigQuery, dbt).
• Experience with data pipeline orchestration tools (e.g., Airflow).
• Solid understanding of cloud platforms (AWS or GCP).
• Strong problem-solving and analytical skills.
• Excellent communication and collaboration skills.

PREFERRED QUALIFICATIONS

• Experience with data security and privacy best practices.
• Knowledge of data visualization tools (e.g., Data Studio, Looker).
• Familiarity with machine learning and artificial intelligence concepts.
• Experience with DevOps practices and tools.

• Data Architecture: Design, implement, and maintain scalable data architectures, including data pipelines, warehouses, and data lakes.
• ETL Development: Develop and optimize ETL processes to efficiently extract, transform, and load data from various sources.
• Data Pipeline Orchestration: Utilize Airflow to orchestrate complex data pipelines, ensuring reliable and timely data delivery.
• Performance Optimization: Continuously monitor and optimize data infrastructure performance, including query optimization and pipeline tuning.
• Data Quality Assurance: Implement robust data quality checks and validation processes to ensure data accuracy and integrity.
• Data Modeling: Design and implement effective data models that support business analytics and reporting.
• Collaboration and Mentorship: Work closely with data scientists, analysts, and other stakeholders to understand data requirements and provide technical guidance.
• Cloud Technologies: Leverage cloud platforms (AWS or GCP) to build scalable and cost-effective data solutions.