Tech Analyst, Data and AI

Air Canada • Dorval, QC

1d

About The Position

Join Air Canada—an iconic Canadian brand and North America’s top-ranked airline—as we lead the next wave of digital transformation in aviation. With major IT initiatives underway and a strong commitment to innovation, this is a unique opportunity to help shape the future of travel technology. We’re looking for a highly skilled and experienced Data Engineering Technical Analyst to drive the design, development, and delivery of scalable data solutions. This role involves leading technical assessments, mentoring data engineers, and actively developing and reviewing data pipelines to ensure robustness, scalability, and performance across enterprise data platforms. The ideal candidate will possess deep expertise in cloud-based data engineering, DevOps practices, and modern data architecture, with a strong focus on Azure and Snowflake ecosystems. As a champion of technical excellence, the Technical Analyst contributes to a culture of innovation, collaboration, and continuous improvement. You’ll work closely with architects, developers, and product teams to enhance operational efficiency, ensure platform stability, and explore emerging technologies—including AI and data-driven insights—that help keep our airline systems modern, scalable, and customer-focused. Let your career take flight by joining a team that’s redefining the digital passenger experience at the forefront of global aviation.

Requirements

• 3-5 years of experience leading enterprise data warehouse development teams.
• Proven success in Agile environments and cloud-based data platforms, especially Azure and Snowflake.
• Expertise in building robust, scalable data pipelines for batch and streaming data.
• Proficiency in SQL, Python, stored procedures, and scheduling tools.
• Hands-on experience with ETL/ELT tools such as Azure Data Factory (ADF), Databricks, Snowflake, DBT and Talend.
• Skilled in implementing monitoring and alerting mechanisms for data pipelines.
• Strong capability in reviewing engineering deliverables for performance, scalability, and maintainability.
• Experience with prompt engineering and leveraging Generative AI (GenAI) to accelerate development and automate engineering workflows.
• Bachelor’s degree in Engineering, Computer Science, Mathematics, or a related field.
• Excellent communication, problem-solving, and analytical skills.
• Proven leadership and mentoring capabilities.
• Strong collaboration skills with cross-functional teams.
• Demonstrate punctuality and dependability to support overall team success in a fast-paced environment.
• Candidates must be eligible to work in the country of interest at the time any offer of employment is made and are responsible for obtaining any required work permits, visas, or other authorizations necessary for employment. Prior to their start date, candidates will also need to provide proof of their eligibility to work in the country of interest.

Nice To Haves

• Based on equal qualifications, preference will be given to bilingual candidates.

Responsibilities

• Manage implementation of solutions for exploratory data analysis and conducts implementation assessments based on Architecture Overview Documents (AOD) and Detailed Data Solutions.
• Provide accurate effort estimations and assess feasibility of proposed architectures and data models.
• Design and develop cost-effective, scalable ELT pipelines using reusable components and frameworks.
• Document pipeline implementation approach including scheduling, dependencies, error handling, monitoring, and alerting.
• Monitor and resolve alerts/failures in prod/non-prod environments.
• Review and approve pipelines developed by data engineers.
• Set up non-production and production environments including MFT flows, database/schema configurations, RBAC, cloud services, Git repositories, and defining appropriate branching strategies.
• Collaborate with stakeholders on UAT and production release strategies.
• Mentor and guide data engineers on best practices, business logic implementation, and quality assurance.
• Scramble and generate mock data for testing purposes.
• Identify and document technical debt; differentiate between defects and scope changes and effectively communicate with product team
• Lead deployment and transition-to-operations (TTO) activities for UAT and production.
• Apply strong understanding of DevOps processes, Star Schema, Data Vault, and data warehousing principles.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level