Sr. Solution Architect

Role overview

Solution design and architecture

• Assess business and technical requirements and define end-to-end data and AI solutions.
• Own architecture for data processing, storage, integration, consumption, security, and AI capabilities.
• Create high-level designs and contribute to detailed design documents.
• Evaluate options against business needs, technical constraints, cost, scalability, performance, and operations.
• Identify risks, dependencies, assumptions, and design trade-offs early.
• Design conceptual, logical, and physical data architectures and models for performance, interoperability, and reuse.
• Define patterns for structured, semi-structured, and unstructured data, including batch, streaming, API, and event-driven integration.
• Develop reusable patterns for GenAI, RAG, enterprise and vector search, document processing, semantic models, and AI agents.
• Design secure integrations across data platforms, AI/ML platforms, LLMs, APIs, applications, and analytics.
• Incorporate identity, RBAC, least-privilege access, classification, privacy, and governance requirements.

Delivery and collaboration

• Guide project teams from discovery through implementation and production readiness, ensuring designs are delivered as intended.
• Participate in design reviews and provide technical direction when implementation decisions need architectural guidance.
• Support transition of AI solutions from proof of concept to governed production.
• Align architecture with enterprise strategy, security standards, data quality requirements, and stewardship practices.
• Communicate decisions, risks, trade-offs, and recommendations to technical and business stakeholders.
• Partner with AI and Data Science teams on architecture requirements for AI/ML and GenAI solutions.
• Assess existing architectures, identify modernization opportunities, perform impact assessments, and recommend technical-debt remediation.
• Evaluate emerging data and AI technologies through technical assessments and proofs of concept; identify AI-related architectural risks.

Required qualifications

• Degree in computer science, information technology, data management, or equivalent.
• Experience architecting solutions across multiple systems, platforms, or business domains.
• Strong knowledge of data architecture, data structures, modern data platforms, databases, and ETL.
• Hands-on experience with SQL, programming, and traditional and big data.
• Strong cloud experience with platforms and technologies such as Azure, AWS, Databricks, Snowflake, Microsoft Fabric, ADF, Event Hubs, Storage, and Key Vault.
• Experience migrating data from on-premises to cloud environments.
• Knowledge of security, privacy, data governance, access control, and data protection.
• Experience designing AI/ML or GenAI enterprise solutions integrated with modern data platforms.
• Experience with LLMs, RAG, embeddings, vector and semantic search, prompt/orchestration patterns, and AI agents.

Preferred qualifications

• Experience with unstructured and semi-structured data or large-scale transformation and migration programs.
• Cloud architecture certifications such as AWS or Azure; experience with streaming platforms such as Kafka.
• Telecommunications industry experience, stakeholder engagement, or peer mentoring.
• Experience with AI capabilities across Azure AI, Databricks, or Snowflake.

Workplace and schedule

• Regular full-time position with a day shift and no stated travel requirements.
• Corporate employees are expected to work onsite at the Calgary work location.

Selection process