Business Engineer /Business Systems Analyst to support a cross‑functional team that will build software using AI-driven software development. 0190670

Our Vancouver client is seeking a Business Engineer /Business Systems Analyst to support a cross‑functional team that will build software using AI-driven software development. The role sits at the intersection of business, product, data, and engineering, translating business intent into clear, buildable solutions that are consumable by an AI software development process. The Business Engineer will also take on Scrum Master responsibilities for the team, focused on planning, flow, clarity, and removing impediments. 0190670

12 months contract, strong possibilities of extensions, 3 days/week in office

Must Have:

• 5+ years of demonstrated experience in business engineering, business analysis, or similar roles translating business intent into system behaviour on complex technology initiatives (an equivalent combination of education and experience may be considered).
• Experience as ScrumMaster and working with technical developers, Architects etc
• Strong ability to translate business needs into clear, actionable requirements and solution options with AI-enabled, automated, or decision support systems
• Experience working with cross-functional delivery teams in an agile or iterative environment.
• Strong facilitation, communication, and stakeholder-management skills and the ability to explain complex topics in plain language.
• Comfort operating in ambiguity and enabling teams to move from uncertainty to clarity.
• Bachelor’s degree in Computer Science or a related STEM (Science, Technology, Engineering, Math) field.

Nice to have:

• Experience defining decision logic, business rules, and human-in-the-loop workflows.
• Postgraduate education or professional certification related to business analysis, systems thinking, decision analysis, or AI-enabled solution design.

Responsibilities:

Business engineering

• Translate business objectives and constraints into clear problem statements, user stories, acceptance criteria, and decision logic suitable for AI-enabled workflows.
• Partner with product and engineering team members to design and evolve AI-enabled features and workflows.
• Analyze current processes and application behaviour to identify simplification and automation opportunities.
• Define success metrics for AI-enabled features and workflows, and validate outcomes.
• Maintain artefacts that link business intent to delivery (requirements, decision logic, metrics).
• Bring structure and clarity in ambiguous problem spaces by defining scope, decisions, and measurable outcomes.
• Drive prioritization by combining user impact, business value, and technical feasibility of AI solutions, ensuring investment decisions reflect real user outcomes and system constraints.

Delivery flow / Scrum Master responsibilities

• Drive continuous improvement by reducing rework, handoffs, and unclear work intake.
• Own cross‑team planning, dependency tracking, and milestone sequencing for AI‑enabled initiatives, balancing model readiness, data availability, platform constraints, and delivery timelines.
• Facilitate planning, backlog refinement, and review sessions, with an emphasis on outcomes and learning.
• Identify and help remove delivery impediments through collaboration and clear ownership.
• Define and maintain reporting mechanisms that track delivery health, AI readiness, risk, and outcomes for senior stakeholders.

Collaboration and communication

• Connect business stakeholders and technical team members to drive shared understanding.
• Support effective decision‑making through clear analysis, options, and trade‑offs.
• Provide guidance and knowledge transfer to business users and team members.
• Communicate recommendations through clear writing and structured artefacts for senior stakeholder decisions.
• Partner with stakeholders to ensure AI enabled workflows meet governance and data-handling expectations.
• Proactively identify and manage AI‑specific risk (data quality, model drift, explainability gaps, governance constraints) and drive mitigation plans.