Principal Enterprise Data Architect – Semantic Web & Middleware

KPG99 INC; Montreal (administrative region); On-site; CAD 140,000 - 210,000; Full time

Job summary

KPG99 INC in Montreal, Canada is seeking a Principal Enterprise Data Architect to drive the design, governance, and scaling of our next-generation data platform. You will lead the convergence of semantic web technologies, enterprise middleware, and end-to-end data architecture to deliver scalable knowledge graphs and robust data pipelines.

Responsibilities include building production-grade RDF triple stores, ontologies, metadata vocabularies, and high-throughput middleware networks.

Qualifications

• 8+ years in data engineering and architecture.
• 3+ years designing production-grade semantic graph systems.
• Proven leadership and cross-functional collaboration.

Responsibilities

• Lead design and governance of scalable semantic data platforms.
• Develop ontologies, vocabularies, and metadata schemas.
• Implement high-throughput middleware for linked data.
• Collaborate with executives to translate patterns into pipelines.

Skills

Semantic Web

RDF SPARQL

Middleware fabrics

Data Governance

Data Modeling

TOGAF/SABSA

Programming languages

Leadership

Communication

Education

Master's or Bachelor's in CS/DS

Tools

GraphDB

Stardog

Virtuoso

Apache Kafka

NiFi/Airflow

MuleSoft

Jena

JSON-LD

Job description

Job Title: Principal Enterprise Data Architect – Semantic Web & Middleware

Location: Montreal, Canada

Overview

We are seeking a visionary Principal Enterprise Data Architect to spearhead the design, governance, and scaling of our next-generation data platform. In this highly specialized role, you will lead the convergence of three critical domains: Semantic Web Technologies (RDF Triple Stores), Enterprise Middleware, and End-to-End Data Architecture.

You will design scalable knowledge graphs, construct the high-throughput middleware networks that route semantic data, and establish the overarching architectural blueprints that transform raw enterprise data into linked, machine-understandable assets.

Responsibilities

Semantic Web & Ontological Engineering

Design, implement, and scale production-grade RDF Triple Stores and Graph Databases to power enterprise knowledge graphs.

Develop and govern semantic models, including core ontologies, taxonomies, and metadata vocabularies using OWL, RDFS, and SKOS.

Optimize highly complex SPARQL queries, graph traversals, and semantic reasoning engines to ensure low-latency data access.

Architect enterprise middleware layers (message brokers, event-streaming fabrics, and API gateways) engineered specifically to handle linked data, RDF graphs, and JSON-LD payloads.

Design robust ETL/ELT abstraction layers that map and lift structured, semi-structured, and relational data into semantic RDF triple formats (R2RML).

Ensure high-availability (HA), replication, horizontal scaling, and disaster recovery configurations across all middleware runtime platforms and triple store clusters.

Strategic Data Architecture

Define the long-term data architecture strategy, reference models, and technical standards across cloud, hybrid, and on-premises environments.

Enforce strong data governance frameworks, access controls, data provenance, and deterministic data lineage tracing protocols across graph structures.

Collaborate with business executives, product owners, and engineering leads to turn architectural patterns into production-ready pipelines.

Technical Skills

Semantic Technologies & RDF

Graph Ecosystems: Mastery of enterprise RDF Triple Stores and Graph Platforms (e.g., GraphDB, Stardog, Virtuoso, AWS Neptune, or Apache Jena).

Graph Standards: Expert-level command of RDF, SPARQL, OWL, SHACL (for graph validation), and serialization formats (Turtle, JSON-LD, N-Triples).

Middleware & Infrastructure

Event & Message Fabric: Deep experience with streaming and messaging systems (Apache Kafka, RabbitMQ, Confluent, or IBM MQ).

Data Integration: Strong proficiency with data routing orchestration engines (e.g., Apache NiFi, Airflow, or MuleSoft) and semantic transformation frameworks.

Programming: Competency in Python, Java, or Scala for building custom semantic connectors and middleware logic.

Core Data Architecture

Data Modeling: Proven experience spanning relational modeling (RDBMS), NoSQL, dimensional modeling, and semantic graph modeling.

Enterprise Practices: Familiarity with framework methodologies like TOGAF, Zachman, or SABSA.

Experience & Qualifications

Education: Master’s or Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related quantitative field.

Experience: 8+ years in data engineering and architecture, with at least 3+ years designing production-grade semantic graph systems and middleware fabrics.

Leadership: Exceptional cross-functional leadership and communication skills, with a track record of driving large-scale data transformation initiatives.