Senior Manager, Solutions Architecture - Data & AI

Who We Are

Welcome to  TELUS Digital — where innovation drives impact at a global scale. As an award-winning digital product consultancy and the digital division of TELUS , one of Canada’s largest telecommunications providers, we design and deliver transformative customer experiences through cutting-edge technology, agile thinking, and a people-first culture.

With a global team across North America, South America, Central America, Europe, Africa, and APAC, we offer end-to-end expertise across eight core service areas: Digital Product Consulting, Digital Marketing Services, Data & AI, Strategy Consulting, Business Operations Modernization, Enterprise Applications, Cloud Engineering, and QA & Test Engineering.

From mobile apps and websites to voice UI, chatbots, AI, customer service, and in-store solutions, TELUS Digital enables seamless, trusted, and digitally powered experiences that meet customers wherever they are — all backed by the secure infrastructure and scale of our multi-billion-dollar parent company.

Location & Flexibility

This role will be in a Work From Near (Hybrid) capacity based in one of the following offices: Toronto, ON or Vancouver, BC OR in a Work From Anywhere (Remote) capacity with travel to clients and TELUS Digital Solutions offices expected.

The Opportunity

As a Senior Manager, Solutions Architecture with a focus on Data & AI, you'll play a pivotal role in bridging the gap between pre-sales strategy and project delivery. Drawing on your deep knowledge of data ecosystems, machine learning, and AI technologies, you'll help shape, architect, and launch impactful solutions that align with client and executive partner goals. Whether leading architectural discovery for new data and AI initiatives or guiding in-flight projects, you'll ensure solutions are both technically sound and aligned with business objectives and operational needs.

You'll engage with prospective clients to explore how our capabilities can meet their unique data and AI challenges, lead architecture efforts for new engagements, and collaborate closely with delivery teams to bring client visions to life. You'll also provide critical estimates around team composition, build scope, and resource planning—ensuring data and AI projects are realistically scoped and staffed for success.

Between engagements, you'll lend your expertise across teams as a flexible problem-solver, while deepening your knowledge in areas like cloud data platforms (e.g., Databricks, Snowflake, Azure, AWS, GCP) or advanced AI/ML methodologies.

Responsibilities

Presale: Lead scoping, technical design, and work planning during the presale process, articulating our data and AI capabilities to prospective clients.

Delivery: Play the lead role in technical architecture and discovery projects, using consulting methods to refine the data product and technical architecture of client projects. This includes designing scalable data pipelines, machine learning model architectures, and MLOps frameworks.

Subject Matter Expertise: Own a "technology vertical" within Data & AI, encompassing solution design, platform selection (e.g., Databricks, Snowflake, AWS Bedrock), implementation support, and thought leadership. This may include areas such as large-scale data warehousing, real-time analytics, machine learning operations (MLOps), or agentic AI systems.

Roving Catalyst: Enhance existing project teams with architectural account-level guidance or specific subject matter expertise in data engineering, machine learning, or artificial intelligence.

Qualifications

Multi-platform familiarity , with expertise in one or more of the following:

Cloud Data Platforms: Deep experience with Databricks , Snowflake , or other major cloud data warehousing/lakehouse solutions.

Cloud AI/ML Services: Proficiency with cloud-native AI/ML platforms (e.g., AWS Bedrock / Sagemaker, Azure ML, Google AI Platform/Vertex AI).

Backend Cloud Platforms: Strong experience with major cloud providers (AWS, Azure, GCP) for deploying and managing data and AI solutions.

Data & AI Industry Expertise: Specialized knowledge of the data ecosystem, including data ingestion, ETL/ELT processes, data warehousing, data lakes, and real-time analytics.

Machine Learning & AI Acumen: Candidates should be aware of current trends in machine learning (supervised, unsupervised, deep learning), MLOps, and advanced AI concepts like Agentic AI or Large Language Models (LLMs).

Technical Proficiency:

Deep understanding of MLOps principles and tools for continuous integration/continuous delivery (CI/CD) of machine learning models.