Data Knowledge Engineer

Overview

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Position Details

Job Title: Data Knowledge Engineer

Contract Length: 6 Months

Location: Hybrid in Guelph, ON

Federal Government Clearance Level Required: None

Vacancy Type: New Position

About Us

Maplesoft Group is currently seeking a Hybrid Data Knowledge Engineer for our client.

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.

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.
   • 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.
   • 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

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)

Compensation

Salary Range: $85.00 – $115.00 per hour

Application Submission Details

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.

Submission Details

Submission Deadline: Friday, March 20, 2026

How to Apply: Submit your resume (and cover letter) to:

• info@maplesoftgroup.com
• https://www.maplesoftgroup.com/Careers/Career-Opportunities

Maplesoft is an equal opportunity employer and welcomes applications from all qualified candidates. Accommodations are available upon request throughout the recruitment process.

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