Posted 3 days ago
Hybrid! Data Science Developer - Senior, 6 months contract
@ Dimensional Tech Inc.
Toronto, Ontario
Overview
Dimensional Tech Inc. is hiring a senior data science developer for a 6-month contract in Toronto, Ontario, from November 23, 2026 to April 23, 2027. The hybrid role requires three days a week on site and focuses on enterprise data architecture and AI/ML data models on Azure.
Responsibilities:
- Design enterprise data architectures and data models for AI/ML and cloud workloads
- Support data requirements for model training, inference, RAG and feature engineering
- Build data pipelines, integration, APIs and metadata for AI/ML solutions
- Ensure data governance, lineage, quality, security and privacy
Requirements summary:
- Significant experience in enterprise data architecture and data modeling
- Deep expertise in cloud data platforms and data engineering
- Strong knowledge of the Azure data and AI ecosystem
- Experience with Azure Machine Learning and Microsoft Foundry
- Understanding of data governance and information lifecycle management
Tools & skills
Source and dates
Source type not verified · crelate
- Employer posting date
- 2026-11-04
- First seen by our source
- 2026-11-06
- Source record updated
- 2026-11-06
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Job details
| Seniority | Senior |
| Department | Software development |
| Work setting | Office |
| Position | Mostly sitting |
| Physical demands | physical low · computer high |
| Languages | English |
Summary and details written by AI from the posting; the employer’s posting is the authority.
Dimensional Tech Inc.
| Field | Value |
|---|---|
| Open jobs on Jobsy | 172 |
• Location: Toronto, Ontario
• Duration: 2026-11-23 to 2027-04-23 ( 110 days)
• Job Type: Hybrid, 3 days onsite per week
• Openings: 1
Requirements:
• Significant experience designing enterprise data architectures and conceptual, logical, and physical data models for AI/ML, Generative AI, analytics, and cloud workloads, including the data requirements for model training, inference, RAG, feature engineering, and AI/ML pipelines.
• Deep technical expertise in modern cloud data platforms, data engineering, integration, APIs, metadata, lineage, and data quality, with strong knowledge of the Azure data and AI ecosystem, Azure Machine Learning, and Microsoft Foundry/Azure AI Foundry, and architectural patterns supporting scalable AI/ML solutions.
• Strong understanding of data governance, data ownership, lineage, data quality, security, privacy, and information lifecycle management, with the ability to design governed, traceable, and fit-for-purpose data architectures that support the AI/ML and model lifecycle.
Journal
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