Senior Manager Data & AI Technical Strategy Lead

About the Role We are seeking a senior, credible, and pragmatic Data and AI leader to provide technical and delivery leadership for complex enterprise modernization programs. This role provides strategic and technical leadership across data architecture, data engineering, analytics, data governance, master and reference data management, identity resolution, and artificial intelligence. The successful candidate can engage confidently with senior client and account stakeholders, navigate complex environments, establish a clear point of view grounded in industry practices, and translate strategy into measurable business outcomes. The role builds strong relationships across client stakeholders, delivery teams, technology partners, and vendors to translate business priorities into scalable, secure, resilient, and supportable solutions. It establishes architectural direction, delivery standards, governance practices, and reusable solution patterns for modern data ecosystems, including environments subject to stringent security, privacy, accessibility, audit, and regulatory requirements. In addition to leading successful delivery, the role supports business growth by shaping opportunities, engaging senior business and technology executives, partnering with sales teams, developing market offerings, and growing high‑performing teams. The individual serves as a trusted Data and AI advisor to clients, technology partners, sales, solutioning, architecture, and delivery teams.

What You'LL Do

Enterprise Data Architecture

• Lead the definition and evolution of current-state, transition-state, and target-state data architectures across cloud, hybrid, operational, analytical, and real-time environments.
• Provide architectural guidance for Microsoft Azure, Microsoft Fabric, OneLake, Power BI, Microsoft Purview, Azure integration services, databases, Databricks, and related technologies.
• Define patterns for batch ingestion, streaming, event-driven integration, data replication, virtualization, mirroring, APIs, data sharing, and interoperability.
• Guide the implementation of data lakes, lakehouses, warehouses, operational data stores, medallion architectures, domain and enterprise data models, semantic models, and data products.
• Ensure solutions address scalability, performance, availability, recoverability, security, privacy, interoperability, maintainability, observability, and cost management.
• Evaluate solution options and recommend technologies and patterns based on business value, functional and non-functional requirements, delivery risk, security considerations, and total cost of ownership.

Delivery Leadership and Assurance

• Provide architecture and delivery oversight across multiple projects, workstreams, and multidisciplinary teams.
• Review solution designs, technical specifications, estimates, implementation plans, non-functional requirements, testing strategies, and operational readiness.
• Facilitate discovery sessions, architecture workshops, design reviews, technical decision forums, and stakeholder presentations.
• Identify architectural risks, delivery dependencies, integration constraints, capability gaps, and practical mitigation options.
• Ensure proposed solutions are implementable, testable, secure, supportable, and aligned with approved architecture and delivery commitments.
• Support Agile delivery through backlog refinement, requirements definition, acceptance criteria, definitions of ready and done, architecture governance, and release planning.
• Provide escalation support for complex architecture, integration, data quality, security, privacy, performance, and reliability issues.

Stakeholder and Partner Management

• Build effective relationships with business leaders, enterprise architects, security and privacy teams, data stewards, product owners, application teams, vendors, and program leadership.
• Coordinate client, partner, vendor, and internal teams to maintain architectural alignment and resolve cross‑functional issues.
• Translate complex concepts into clear business language for executive and non‑technical audiences.
• Present recommendations, trade‑offs, risks, costs, assumptions, and implementation options to governance bodies and senior stakeholders.
• Promote collaboration across business, architecture, engineering, analytics, governance, operations, security, and change‑management teams.

Data Governance and Information Management

• Lead or support enterprise data governance, metadata management, data cataloguing, lineage, data quality, stewardship, classification, retention, and access‑control capabilities.
• Provide guidance on Microsoft Purview, OneLake Catalog, and the operating processes required to make governance effective.
• Define data ownership, stewardship responsibilities, business glossaries, classifications, quality rules, controls, and data‑product accountability.
• Ensure architecture and delivery practices support privacy, records and information management, security, auditability, accessibility, and applicable regulatory obligations.
• Promote data discoverability, trusted reuse, consistent definitions, responsible access, and evidence-based governance across programs and delivery teams.

Master Data, Reference Data, and Identity Resolution

• Provide leadership for master data management, reference data management, customer or client identity resolution, entity matching, and 360‑degree data capabilities.
• Guide scalable approaches for matching, survivorship, deduplication, data quality, hierarchy management, and golden‑record creation.
• Support decisions involving deterministic and probabilistic matching, fuzzy matching, identity services, biometric or photo‑match integrations, and high‑volume or high‑velocity client information.
• Ensure identity‑related solutions include appropriate privacy controls, security safeguards, explainability, audit trails, exception handling, and human‑review processes.
• Balance match accuracy, latency, throughput, operational resilience, scalability, maintainability, and cost.

Analytics and Business Intelligence

• Guide the design of enterprise reporting, Power BI semantic models, reusable datasets, dashboards, operational reports, and advanced analytical products.
• Promote clear separation among source‑system data, ingestion and transformation layers, curated data products, enterprise semantic models, and presentation‑layer reporting.
• Establish patterns that improve reporting consistency, data quality, performance, row‑ and object‑level security, certification, and reuse.
• Translate reporting and analytics needs into measurable requirements, conceptual and logical models, acceptance criteria, and delivery backlogs.
• Support the modernization of legacy reporting and analytics platforms using cloud‑based, governed, and reusable capabilities.

Team and Capability Development

• Coach and provide technical direction to architects, data engineers, analysts, data scientists, developers, consultants, and delivery leads.
• Support resource planning, role definition, onboarding, performance development, succession planning, and team capability assessments.
• Develop internal knowledge through reference architectures, playbooks, demonstrations, reusable assets, and communities of practice.
• Promote consistent engineering standards, documentation, peer review, automation, testing, DevSecOps, and continuous improvement.
• Lead and grow internal people while supporting career development and a strong technical culture.

Functional Leadership and Requirements

• Develop a strong understanding of the client's business, processes, data, policies, controls, and operating context.
• Work with business stakeholders and product owners to define detailed business, data, reporting, governance, and non‑functional requirements.
• Bridge business strategy, product ownership, architecture, and technical delivery.
• Build trusted stakeholder relationships to elicit requirements that can be traced into design, build, testing, deployment, and production support.
• Develop business‑domain and conceptual data models, data definitions, and data dictionaries that inform logical and physical modelling.
• Provide thought leadership on the business value, feasibility, risks, and adoption considerations of data‑driven and AI‑enabled solutions.

Strategic and Program Leadership

• Provide strategic direction for data, analytics, governance, and AI capabilities across major client programs and accounts.
• Act as a trusted advisor to client executives, program leaders, architects, product owners, and delivery teams.
• Align data initiatives with business priorities, enterprise architecture principles, modernization roadmaps, and desired customer, citizen, and operational outcomes.
• Identify opportunities to improve service delivery, operational efficiency, decision‑making, risk management, and program integrity through data and AI.
• Contribute to account planning, opportunity shaping, solution development, estimation, proposals, statements of work, commercial discussions, and executive presentations.
• Develop and promote reusable architecture patterns, accelerators, templates, methods, standards, and delivery assets.

Business Growth and Market Leadership

• Support presales and solutioning for Data and AI opportunities, from qualification and discovery through proposal and contracting.
• Lead solution architecture for new opportunities and pursue relevant architecture or platform certifications.
• Contribute to go‑to‑market offerings, industry propositions, thought leadership, demonstrations, and reusable sales assets.
• Build strong relationships with technology partners and partner engineering or sales organizations.
• Run executive briefings, discovery sessions, design‑thinking workshops, and technical working sessions.
• Provide Data and AI advisory services and lead delivery across multiple clients and industries.

What You'll Bring

• 15+ years of experience across data warehousing, data engineering, data migration and modernization, data architecture, artificial intelligence, machine learning, data science, advanced analytics, or related technology‑consulting disciplines.
• Established professional credibility in the Data and AI solution space, with the ability to engage confidently with senior business and technology executives.
• Proven experience in consulting, professional services, technology advisory, systems integration, or a comparable client‑facing environment.
• Demonstrated success in opportunity origination, presales, business development, solution selling, or consulting sales.
• Experience shaping complex Data and AI programs through discovery, qualification, architecture, solution development, estimation, proposal creation, presentations, commercial discussions, and mobilization.
• A track record of expanding strategic client relationships and converting client priorities into new work.
• Strong understanding of generative AI, large language models, agentic AI, multi‑agent solutions, enterprise AI adoption, and AI‑enabled business processes.
• Delivery experience with enterprise Data and AI programs, including architecture, implementation, deployment, adoption, governance, operations, and value realization.
• Strong executive presence, communication, facilitation, storytelling, negotiation, and presentation skills.
• Sound commercial judgement and the ability to balance client value, technical feasibility, delivery risk, responsible AI, and commercial outcomes.

Technology and Domain Experience The ideal candidate will demonstrate deep expertise in core data disciplines and credible experience across several of the following areas:

• Enterprise data warehousing, including dimensional modelling and Kimball methodology.
• Microsoft data ecosystem, including Microsoft Fabric, OneLake, Microsoft Purview, Power BI, Azure Data Factory or Fabric Data Factory, Azure data services, and Microsoft AI services.
• Generative AI, large language models, retrieval‑augmented generation, agentic AI, multi‑agent architectures, prompt and evaluation practices, and model lifecycle considerations.
• Data pipeline design and scaling using Fabric Data Factory, Spark, Python, notebooks, SQL, and Databricks.
• Master data management, reference data management, identity resolution, data quality, survivorship, and golden‑record implementation.
• Data governance strategy and delivery, including cataloguing, metadata, lineage, classification, stewardship, access controls, and quality management.
• Operational, analytical, conceptual, logical, physical, dimensional, domain, enterprise, and semantic data modelling.
• Enterprise integration patterns, including event‑driven, API‑led, publish‑subscribe, point‑to‑point, batch, streaming, replication, CDC, and data virtualization.
• Microsoft Dynamics 365, Dataverse, Power Platform, and integration with enterprise data platforms.
• Application architecture and development principles, including APIs, microservices, security, testing, DevSecOps, source control, CI/CD, observability, and supportability.
• Responsible AI, privacy, cybersecurity, model and data risk management, explainability, human oversight, and regulatory compliance.
• Cloud architecture considerations, such as landing zones, networking, identity and access management, encryption, secrets management, business continuity, disaster recovery, FinOps, and platform operations.

What Makes You Successful

• You are recognized as a credible, pragmatic, and outcome‑oriented Data and AI leader.
• You are energized by building relationships and creating opportunities, not only by delivering work that has already been sold.
• You can enter an ambiguous client conversation, identify the underlying business problem, and create a clear path from discovery to delivery.
• You connect technology capabilities to measurable business outcomes and adoption needs rather than leading with technology alone.
• You are comfortable engaging C‑suite stakeholders, business leaders, enterprise architects, product owners, and engineering teams.
• You combine strategic thinking, technical depth, commercial judgement, delivery discipline, and clear executive communication.
• You know when to challenge assumptions, when to simplify a solution, and how to sequence delivery to accelerate value and reduce risk.
• You collaborate effectively in a matrixed environment and mobilize the right industry, sales, partner, solutioning, architecture, and delivery specialists.
• You understand that successful Data and AI transformation requires trust, relevance, differentiation, governance, adoption, and a credible path from vision to production.

Benefits and Total Rewards | Avanade Canada

Avanade is the leading global provider of digital, cloud, AI and advisory services, industry solutions and design‑led experiences across the Microsoft ecosystem. Founded in 2000 as a joint venture between Accenture LLP and Microsoft Corporation, Avanade has more certifications, Gold‑level competencies and most valued professionals (MVPs) than any other Microsoft partner.

Compensation at Avanade varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. As required by local law, Avanade provides a reasonable range of compensation, based on full‑time employment, for roles that may be hired as set forth below.

British Columbia / Ontario - CAD177,500 - CAD210,800

We anticipate this job posting will be posted on 10/23/2026 and open for at least 30 days .

At Avanade, we advance the world through the power of people and Microsoft. Join us to shape your skills and the future with bold ideas and innovative solutions. Our partnership with Microsoft and Accenture empowers you to unlock your full potential. Working at Avanade offers the chance to build a career path that suits your skills and interests and the freedom to be your authentic self. We are committed to your growth, well‑being, and success. This is where passion meets opportunity, technology meets humanity, and every challenge is a chance to make a genuine impact on colleagues, clients, and communities. Together, we do what matters.