Top Tech Jobs & Startup Jobs in Toronto

Looking for a Data Engineer experienced in Dataiku to design and maintain data pipelines and infrastructure, ensuring data flow and integration across teams.

The Data Engineer will architect and implement advanced analytics capabilities, manage data pipelines, and optimize data processing using AWS cloud and various analytical tools.

Build high-performance API services and production-grade Generative AI systems. Design and implement end-to-end RAG pipelines, vector stores, embeddings, and latency-optimized generators. Develop agentic AI using LangChain/CrewAI/Semantic Kernel, integrate LLM APIs and AWS Bedrock, manage CI/CD and ML pipelines, and apply evaluation frameworks to detect hallucinations and optimize performance.

Seeking an experienced Data Engineer to design and maintain data pipelines and infrastructure, collaborating with data scientists and stakeholders.

The Machine Learning Engineer will create scalable ML systems, build production data pipelines, and ensure robust deployment and monitoring of solutions.

Manage large-scale data analytics engagements, collaborate with technology teams, and present solutions to stakeholders to drive business value.

The role involves engaging with client teams to gather and analyze requirements, managing projects, acting as a point of contact, and collaborating with offshore teams to deliver analytical solutions.

The Manager, BI Consulting will lead BI initiatives, engage stakeholders, analyze requirements, design data solutions, and manage client relationships.

The Data Product Manager will lead the development of enterprise data products by translating business needs into scalable data solutions, managing analytics projects, and coordinating with global teams to ensure efficient delivery and optimization of data initiatives.

Support stakeholders by profiling, mapping, and validating financial reconciliation datasets. Build complex SQL queries and Python workflows, and use Snowflake and Databricks to design, transform, and manage large-scale finance data for reporting, reconciliation, and analytics. Ensure data quality, perform root-cause analysis, and support governance and documentation.