4 month contract + extensions Hybrid 3x per week in Toronto Pay: $75-85/hr incorp Must Haves 7+ years of experience in data engineering and/or AI engineering with a strong focus on building end-to-end data pipelines Proven experience designing and building AI/LLM-based solutions, including agentic AI frameworks or AI agents Hands-on experience with Python and PySpark for large-scale data processing and pipeline development Strong experience with Snowflake, including data modeling, transformations, and enabling downstream AI/analytics use cases Experience building and orchestrating pipelines using Apache Airflow (DAGs, scheduling, dependencies) Experience integrating AI capabilities into existing data pipelines (e.g., enabling AI from structured data sources) Strong understanding of ETL development and building pipelines from ingestion through deployment Experience working with distributed data processing/tools such as Apache Spark and related ecosystem Ability to own development from design through deployment, including pipeline buildout Strong communication skills and ability to operate as an SME for AI-related components within a data team

Nice to Haves Experience working with tools such as Jupyter, Apache ecosystem tools, or AI platforms/environments (e.g., internal AI farms) Exposure to building agentic AI frameworks or multi-step AI workflows Familiarity with modern data ecosystem tools (DBT, Iceberg, etc.) Experience working in a large enterprise environment General industry experience (financial services or otherwise), though not required Exposure to cloud platforms such as AWS or Azure Job Description Insight Global is seeking a Data & AI Engineer to join a high-performing data team focused on building next-generation AI-enabled data solutions. This individual will operate at the intersection of data engineering and artificial intelligence, designing and developing end-to-end pipelines that power advanced analytics and agentic AI capabilities.

The ideal candidate will have a strong foundation in data engineering, with experience building scalable ETL pipelines using tools such as Python, PySpark, Snowflake, and Apache Airflow. This role will involve enabling AI capabilities within existing data pipelines, as well as contributing to the development of AI agents and emerging agentic AI frameworks.

Day-to-day, the engineer will design, build, and deploy data pipelines, integrate AI-driven functionality into structured datasets, and support the development of scalable, production-grade AI solutions. This role requires someone comfortable owning the full lifecycle—from pipeline development and orchestration to deployment and optimization.

This is an opportunity to work on cutting-edge AI initiatives within a data-focused environment, helping shape how AI is operationalized across large-scale data systems while collaborating closely with cross-functional engineering and analytics teams. We may use artificial intelligence tools to assist with the screening, assessment, or selection of potential applicants for this position.