Data Engineer

Requirements

• Design, build and support the data and ETL pipelines
• AI governance framework
• Design logical and physical data models for warehouse
• Collaborate with Application owners, data architects, data scientists and other SMEs
• Design scalable data infrastructure required to support AI/ML solutions
• Integrate new data management technologies and software engineering tools into existing structures to support Analytics and AI use cases
• Handle the performance tuning of data loads and databases
• Identify, analyze and interpret trends or patterns in complex data sets
• Ensure reliable execution and monitoring of nightly data pipeline jobs
• Troubleshooting failures, optimizing performance, and maintaining data integrity across systems
• Liaise with the Release Lead to implement, monitor and improve the MLOps process within an Agile framework
• Translate business needs into data model strategies with the support of SMEs
• Incorporate core data management competencies
• Design AI-ready ecosystems that are flexible, scalable and resilient
• Support both structured and unstructured data for Machine learning and AI use cases
• Assist in the design and development of databases and data marts
• Provide technical assistance in identifying, evaluating, and developing systems and procedures
• Coordinate with business owners, and data users to better understand data and information needs

• A minimum of 3 years of hands-on experience with data engineering and MLOps
• Bachelors degree in computer science, engineering or a related field
• Strong programming skills in languages such as Python, Java, or Scala.
• Knowledge of AI/ML frameworks such as Tensor flow, Pytorch, or MLflow
• Understanding of generative AI and advanced analytics approaches, as AI increasingly influences end to end data workflows
• Proficiency in SQL and experience with relational database systems (e.g., MS SQL Server).
• Experience with cloud platforms (e.g., AWS, Azure) and their data services.
• Experience with data modeling, ETL development, and data warehousing solutions.

• Problem solving
• Great communication
• Professional curiosity
• Able to take initiatives
• Self-starter
• AI governance

• Bonus: up to 7% of annual salary

• Vacation: 15 days accrual
• Personal days/ Float days: 4 days accrual

• Hybrid
• This position follows a hybrid working policy, requiring two (2) days per week in our office and three (3) days working remotely. In order to assist with onboarding and workplace introductions, new hires will be required to work four (4) days in office per week during their first three (3) months of service. (All remote work must be completed from your home office within the province of Ontario.)