Data Engineer (Yonge Mills)

Responsibilities

Design, develop, and manage ETL data pipelines that facilitate the detailed extraction, transformation, and loading of data from diverse sources. Design and execute robust data quality checks and implement and maintain a curated feature store. Deploy and scale existing machine learning models in production, ensuring robust data ingestion, updates, and model scoring. Collaborate closely with data scientists to provide clean, well‑structured data for modelling and to scale and automate experimental models for production use. Work with external data engineering teams for infrastructure setup and database management, while handling project‑specific data needs internally. Collaborate with external teams to ensure infrastructure aligns with deployment requirements while owning the ML model lifecycle internally. Qualifications

Master’s degree in computer science, software engineering, or an equivalent technical field. 2+ years of data engineering experience with Azure‑related data technologies. Proven experience deploying and managing AI models in production environments. Hands‑on knowledge of Azure Databricks, Azure Data Factory, Azure Data Lake Storage, Synapse Serverless/dedicated/spark pools, Python, PySpark, and MLflow, and developing scripts for ETL processes and automation in Azure Data Factory and Azure Databricks. Solid understanding of model performance metrics and evaluation techniques. Proficiency in programming languages commonly used in ML development such as Python and R. Experience with model deployment frameworks (e.g., TensorFlow Serving), containerization (Kubernetes, Docker), and cloud platforms (Azure). Familiarity with DevOps practices and tools for CI/CD pipelines (GitLab, Jenkins) in the context of deploying AI models. Understanding of Azure infrastructure, subscription management, resource groups, role‑based access control, Azure AD integration, and Azure security principles (user group, service principal, managed identity), password/credential/key management, and data protection. Excellent problem‑solving skills focused on root cause analysis and continuous improvement. Excellent oral and written communication skills with the ability to convey technical and business concepts, and strong presentation skills. Adaptability and willingness to learn new deployment and ETL updates. Ability to lead technical presentations, demonstrations, workshops, design sessions, proofs of concept, and pilots. Preferred Qualifications

Experience with data modelling, data mart, data lakehouse architecture, SCD, data mesh, and Delta Lake. Knowledge of distributed computing frameworks (e.g., Apache Spark) for large‑scale data processing. Experience with deep learning frameworks (e.g., PyTorch) and custom deep neural network solutions. Perks

Comprehensive benefits and retirement programs. Performance incentives. Continuing Education Programs. Perks supporting employee well‑being. Career growth opportunities and product discounts. Compensation

$64,000 – $106,000 per annum. Salary decisions consider experience, knowledge, skills, market location, industry benchmarks, internal equity, and role‑specific requirements. Equal Employment Opportunity

We are committed to fostering an environment where belonging thrives, and diversity, inclusion, and equity are infused into everything we do. Accommodations are available for candidates with disabilities or other needs during the application and interview process.

#J-18808-Ljbffr Apply on Kit Job: kitjob.ca/job/2pqw3z