Title Data Knowledge Engineer
Location Hybrid in Guelph, ON
Start Date 03-30-2026
Language English
Salary $85.00 – $115.00 per hour
Security Clearance None
Duration 6 Months
Date Posted 03-12-2026
Job ID 14146
Recruiter Email info@maplesoftgroup.com
Job Title: Data Knowledge Engineer
Contract Length: 6 Months
Location: Hybrid in Guelph, ON
Federal Government Clearance Level Required: None
Vacancy Type:
New Position
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About Us: Maplesoft Group is currently seeking a Hybrid Data Knowledge Engineer for our client.
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Position Summary:
The Data Knowledge Engineer plays a critical role in enabling the organization’s transition toward AI-driven analytics and data navigation. This role is responsible for designing, developing, and maintaining the enterprise data knowledge graph—a structured network of metadata, business definitions, relationships, and governance policies that enables AI systems and analytics platforms to accurately interpret and navigate enterprise data.
Working within the Data Governance team, the Data Knowledge Engineer ensures that enterprise data assets are described, connected, and governed in a way that supports trusted analytics, AI-driven insights, and responsible data usage across the organization.
This role bridges data governance, data architecture, and analytics engineering to ensure that business meaning, data ownership, policies, and lineage are represented in a machine-readable format that supports modern data platforms and AI interfaces.
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Key Responsibilities
1. Enterprise Data Knowledge Graph Development
Design and maintain the enterprise data knowledge graph by connecting metadata across the organization’s data ecosystem. Responsibilities include:
Defining relationships between data assets, business concepts, policies, and ownership
Modeling connections between data products, datasets, semantic models, metrics, and domains
Ensuring metadata relationships support AI-driven data discovery and navigation
Maintaining graph integrity as data platforms and architectures evolve
2. Metadata Integration and Architecture
Integrate metadata across enterprise platforms to create a unified metadata ecosystem.
Metadata sources may include:
Data catalog platforms (e.g., Microsoft Purview)
Lakehouse governance platforms (e.g., Databricks Unity Catalog)
Analytics and semantic layers (e.g., Microsoft Fabric semantic models)
Data pipelines and lineage systems
Identity and access management systems
Ensure metadata relationships are captured and represented consistently across the ecosystem.
3. Semantic Model Alignment
Collaborate with analytics, data engineering, and domain teams to ensure semantic models accurately represent business meaning.
Responsibilities include:
Aligning semantic models with business glossary definitions
Supporting standardization of enterprise metrics
Ensuring consistent interpretation of key business concepts across analytics assets
Connecting semantic models to underlying data products and datasets
4. Governance Policy Enablement
Translate governance policies into machine-readable metadata and policy structures that support automated enforcement.
Examples include:
Data classification rules
Access control policies
Regulatory restrictions
Trust signals and certification indicators
This work supports dynamic governance capabilities, such as AI-mediated data access and trust-based query evaluation.
5. Data Product Context and Ownership
Support the governance framework for enterprise data products by ensuring that critical metadata elements are defined and maintained.
Examples include:
Data product ownership
Steward assignments
Domain alignment
Certification status
Data quality indicators
These elements become core nodes within the enterprise knowledge graph.
6. AI Readiness and Data Context
Ensure enterprise data assets are described in ways that allow AI systems and copilots to interpret them accurately.
This includes ensuring that:
Business definitions are clear and standardized
Authoritative datasets are identified and documented
Data relationships are well understood
Governance policies are visible to AI navigation systems
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Required Qualifications
Experience with enterprise metadata management and data catalog platforms
Knowledge of modern data architectures (lakehouse, medallion, data products)
Experience with semantic data modeling and metrics layers
Familiarity with data lineage, metadata frameworks, and governance concepts
Ability to model relationships between data assets, business meaning, ownership, and policies
Experience integrating metadata across multiple enterprise data platforms
Strong collaboration skills across technical and business teams
Preferred technologies:
Microsoft Purview
Databricks Unity Catalog
Microsoft Fabric
Knowledge graph or ontology modeling concepts
Data governance frameworks (DAMA, DCAM)
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Compensation
Salary Range: $85.00 – $115.00 per hour
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Our recruitment process is led by human recruiters who review all applications and make the final hiring decisions. We use AI-assisted tools to help screen and organize applications. These tools do not replace human judgment, and all hiring decisions are made by people.
Please note that data collected by the Company may be stored or processed on servers located outside of Canada.
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Application Submission Details
Submission Deadline:
Friay, March 20, 2026
How to Apply:
Submit your resume (and cover letter) to:
info@maplesoftgroup.com
Or
https://www.maplesoftgroup.com/Careers/Career-Opportunities
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Maplesoft is an equal opportunity employer and welcomes applications from all qualified candidates. Accommodations are available upon request throughout the recruitment process.