Senior Technical Business Analyst

QuadReal Property Group•Toronto, ON

4d•Hybrid

About The Position

The Senior Technical Business Analyst is the requirements and scoping anchor for AI&DI's internal build program. This role translates business problems into well-defined technical specifications, ensures delivery decisions are documented with full rationale, and bridges the gap between business stakeholders and a technical team working on AI, ML, and data platform initiatives. This is a hands-on, delivery-embedded role, not a governance-only or documentation-support function.

Requirements

6–8 years of business analysis experience, with at least 4 years in a technical or data-intensive context (AI, ML, data platform, enterprise software).
Actively uses AI tools (Claude, Copilot, Cursor, or equivalent) as a daily work habit.
Demonstrable ability to write functional requirements and acceptance criteria that engineering teams can build to without ambiguity.
Working knowledge of data concepts (pipelines, APIs, data models, SQL) sufficient to engage engineers as a peer.
Experience scoping projects with explicit out-of-scope documentation and stakeholder sign-off.
Strong facilitation skills - comfortable leading requirements workshops with mixed business and technical audiences.
Familiar with agile delivery: sprint ceremonies, backlog management, story writing.

Nice To Haves

Experience working alongside ML or AI engineering teams.
Familiarity with LLM-based systems, RAG architectures, or AI agents.
Background in real estate, asset management, or financial services.
Experience in a buy-first / vendor-oversight delivery model.

Responsibilities

Lead requirements-gathering sessions with business stakeholders, translating priorities into clearly scoped functional and technical specifications.
Define and document scope boundaries (what is in scope, what is explicitly out of scope, and why) for every internal AI build.
Maintain a decision history for each initiative to ensure future enhancements don't require rediscovering prior assumptions or constraints.
Write acceptance criteria that engineers and QA can act on, not just business descriptions.
Work directly with the AI Architect and Engineers to validate that requirements are technically feasible and appropriately scoped.
Define data requirements (data sources, quality expectations, volume, latency) feeding directly into engineering and ML pipeline work.
Pressure-test solution designs against stated business requirements before development begins.
Participate in sprint planning and reviews, raising requirement gaps or misalignments early.
Share team-wide accountability for user and operational support, and contribute to the continuous improvement of processes, practices, and products built and managed by AI&DI.
Maintain solution specification documents (functional requirements equivalent) for all active and completed internal builds.
Ensure all artifacts are stored in shared, accessible locations (Teams, ClickUp, shared Claude projects), not siloed in email or personal drives.
Support knowledge transfer and handover readiness so new team members or external vendors don't require oral history orientation.
Contribute to standardizing templates for requirements, decisions, and support expectations across projects.
Act as the primary bridge between business unit contacts and the technical delivery team across AI&DI initiatives.
Support intake - Stage 1 (Discovery) and Stage 3 (Planning) of the operating model, ensuring business problems are well-framed before engineering begins.
Track and report delivery outcomes (are deployed solutions being used, for what, and how often) framed as outcomes accountability, not product analytics.
Coordinate with central IT and support stakeholders to ensure alignment on internal vs. vendor-supported solution policies.