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This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Architect Data Engineer based in Canada.
This role sits at the intersection of data architecture, AI platform engineering, and enterprise-scale system design, with a strong focus on enabling next-generation Agentic AI capabilities. You will define and lead the architectural vision for a modern, high-performance data layer that unifies structured, unstructured, and graph-based data across complex hybrid environments. The position requires deep expertise in designing scalable data systems that support real-time AI workloads, advanced analytics, and knowledge-driven agentic workflows. You will collaborate directly with clients as a technical authority, translating business needs into robust, production-grade architectures. Operating in a highly innovative, fast-paced environment, you will shape data strategies that power enterprise AI transformation. This is a high-impact role combining deep technical leadership with client-facing strategic influence.
Accountabilities:
- Define and lead the end-to-end architecture for a modern data platform supporting Agentic AI, integrating structured, unstructured, and graph-based data systems.
- Design multi-tenant schemas and knowledge graph ontologies to enable advanced reasoning, contextual understanding, and cross-domain data retrieval for AI agents.
- Oversee performance, reliability, and security of large-scale data systems including Snowflake and Kinetica, ensuring high availability for mission-critical workloads.
- Serve as the primary technical advisor for clients, leading discovery workshops and aligning architectural decisions with business and AI strategy goals.
- Establish performance benchmarks for data latency, retrieval accuracy, and system scalability to support real-time agentic execution.
- Design and optimize advanced ETL/ELT pipelines, including streaming, batch, and CDC-based data ingestion strategies.
- Define and enforce database governance, including indexing, partitioning, resource optimization, and cloud-native scaling strategies.
- Collaborate on the design of API-first and tool-enabled data layers for integration with AI agents and LLM-based systems.
Requirements:
- 10+ years of experience in data engineering, data architecture, or platform engineering roles within enterprise or AI-driven environments.
- Strong expertise in architecting hybrid data ecosystems using platforms such as Snowflake, Kinetica, NoSQL, and graph databases.
- Deep knowledge of knowledge graph design, including RDF or property graph modeling for enterprise-scale systems.
- Proven experience designing and optimizing ETL/ELT pipelines, including streaming, batch, and CDC architectures.
- Strong understanding of database internals, including indexing strategies, partitioning, scaling, and performance tuning in cloud environments.
- Experience building data platforms that support real-time analytics, AI/ML, or agent-based systems.
- Client-facing experience as a technical lead, including running workshops, gathering requirements, and defining architectural roadmaps.
- Familiarity with semantic layers (e.g., dbt Semantic Layer, Cube) is a strong plus.
- Knowledge of data security principles such as RBAC and row-level security in distributed systems.
- Strong communication and stakeholder management skills with a consultative mindset.
Benefits:
- Competitive compensation aligned with experience and market standards.
- Remote work flexibility within Canada or the US East region.
- Opportunity to work on cutting-edge Agentic AI and enterprise data platform architectures.
- Exposure to Fortune 500 clients and large-scale AI transformation initiatives.
- Access to advanced cloud, AI, and data technologies in a research-driven environment.
- Strong culture of innovation, learning, and continuous technical upskilling.
- Collaborative environment working alongside highly skilled AI and data engineering teams.
- Opportunity to influence next-generation data architectures powering real-world AI systems.
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