Data Knowledge Engineer

Title: Data Knowledge Engineer

Location: Hybrid in Guelph, ON

Start Date: 03-30-2026

Salary: $85.00 – $115.00 per hour

Duration: 6 Months

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

Enterprise Data Knowledge Graph Development

Design and maintain the enterprise data knowledge graph by connecting metadata across the organization’s data ecosystem.

Define relationships between data assets, business concepts, policies, and ownership.

Model connections between data products, datasets, semantic models, metrics, and domains.

Ensure metadata relationships support AI‑driven data discovery and navigation.

Maintain graph integrity as data platforms and architectures evolve.

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.

Semantic Model Alignment

Collaborate with analytics, data engineering, and domain teams to ensure semantic models accurately represent business meaning.

Align semantic models with business glossary definitions.

Support standardization of enterprise metrics.

Ensure consistent interpretation of key business concepts across analytics assets.

Connect semantic models to underlying data products and datasets.

Governance Policy Enablement

Translate governance policies into machine‑readable metadata and policy structures that support automated enforcement.

Include policies such as:

Data classification rules

Access control policies

Regulatory restrictions

Trust signals and certification indicators

Support dynamic governance capabilities, such as AI‑mediated data access and trust‑based query evaluation.

Data Product Context and Ownership

Support the governance framework for enterprise data products by ensuring that critical metadata elements are defined and maintained.

Define data product ownership, steward assignments, domain alignment, certification status, and data quality indicators.

Make these elements core nodes within the enterprise knowledge graph.

AI Readiness and Data Context

Ensure enterprise data assets are described in ways that allow AI systems and copilots to interpret them accurately.

Ensure business definitions are clear and standardized.

Identify and document authoritative datasets.

Maintain an understanding of data relationships.

Make governance policies 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

Equal Opportunity Statement 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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