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Business Analyst, AI Transformation

Cantor Fitzgerald Technology Markets LLC · New York, United States

About this role

Cantor Fitzgerald Technology Markets LLC seeks a Business Analyst for its AI Transformation team in New York. The role combines business analysis with hands-on implementation to modernize Shared Services through process reengineering, automation and AI-powered solutions. The base salary is $130,000–$170,000.

Responsibilities

  • Work with Shared Services stakeholders to understand and map processes, identify opportunities to improve efficiency and reduce risk, and drive process optimization and reengineering.
  • Shape and prioritize AI, automation and process-transformation use cases based on business priorities, anticipated outcomes, complexity and urgency. Contribute to build-versus-buy decisions.
  • Implement AI and rule-based automation solutions, help integrate enterprise assistants such as Claude and ChatGPT, and provide training and support.
  • For solutions requiring engineering support, gather and refine requirements, develop functional specifications, collaborate with engineering teams and data owners, and help drive implementation.
  • Communicate project progress, risks and outcomes to business and technical teams, and support change management and adoption.

Qualifications

  • At least 3 years of experience in product, technology, data, analytics or similar roles combining business acumen and technical capabilities.
  • A record of implementing scalable, robust AI solutions in corporate environments that deliver business value. Knowledge of the SDLC, LLMs, APIs and agentic workflows, with the ability to work credibly with engineering teams.
  • Strong communication, stakeholder management and practical problem-solving skills; ability to learn new business domains, build trust and deliver in a fast-paced environment.
  • Experience in financial services or other regulated industries is preferred.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Business analysisProcess mappingProcess optimizationProcess reengineeringAI automationRequirements gatheringFunctional specificationsStakeholder managementChange managementSDLCLarge language modelsAPIsAgentic workflowsClaudeChatGPTProblem-solving

Your skill match

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Get interview-ready for this role

A focused preparation guide, built around this job’s responsibilities and requirements.

✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01

Understand the role

What matters most

  • Reengineer Shared Services processes

    Partner with stakeholders to map current processes, identify AI and automation opportunities, and improve efficiency, risk and scalability.

  • Prioritize use cases by business value

    Shape AI, automation and process-transformation use cases; weigh anticipated outcomes, complexity and business urgency, and contribute to build-versus-buy discussions.

  • Deliver solutions hands-on and with engineering

    Implement AI and rule-based automations where appropriate. For more complex work, turn business requirements into functional specifications and coordinate with engineering teams and data owners.

  • Drive adoption and communicate outcomes

    Support enterprise-assistant integration, training and change management while communicating progress, risks and outcomes to business and technical teams.

Likely challenges to think through

  • Choosing where automation belongs

    Preparation hypothesis: A faster process may not be a better one if it increases risk. Prepare an example of how you mapped a workflow, compared improvement options and chose a practical intervention.

  • Balancing quick delivery with robust implementation

    Preparation hypothesis: The hybrid builder role may require deciding when to implement directly and when complexity calls for engineering support. Prepare to explain that boundary and how you would make requirements clear enough for a handoff.

  • Turning a delivered tool into an adopted process

    Preparation hypothesis: Technical delivery alone may not produce the intended benefit. Prepare an example that connects stakeholder trust, training or support, and communication of outcomes to sustained use.

03

Practice your answers

Technical practice

Suggested practice: How would you map a Shared Services process before proposing automation?

What to coverDescribe how you would identify stakeholders, document the current steps and handoffs, and locate delays or sources of risk. Explain how that map would help distinguish a process problem from an automation opportunity.

Suggested practice: How would you prioritize competing AI and automation use cases?

What to coverExplain how you would compare anticipated outcomes, complexity, business urgency and alignment with priorities. Describe what evidence you would seek before recommending which use case to pursue first.

Suggested practice: How would you contribute to a build-versus-buy decision?

What to coverDiscuss how you would compare a tool’s fit with business needs against the effort of building, integrating and maintaining a solution. Explain which questions you would take to stakeholders and engineering before making a recommendation.

Suggested practice: When would you use rule-based automation rather than an LLM solution?

What to coverCompare the approaches for a well-defined Shared Services task, considering how consistently the work can be specified and checked. Explain how you would test whether the chosen approach improves efficiency without increasing risk.

Suggested practice: How would you assess an opportunity to integrate an enterprise assistant such as Claude or ChatGPT?

What to coverDescribe how you would define the intended task, user workflow and criteria for a useful result before implementation. As a preparation suggestion, consider how you would check outputs and identify where human review remains appropriate.

Suggested practice: What would make a hands-on AI implementation robust and scalable?

What to coverExplain how you would evaluate reliability and business value beyond an initial demonstration. Discuss how you would identify failure cases, monitor results and decide whether the solution is ready for broader use.

Suggested practice: How would you discuss APIs and agentic workflows with an engineering team?

What to coverExplain the roles of integrations, data access and workflow steps in a proposed solution. As a preparation suggestion, describe how you would define boundaries and checks so engineers can assess feasibility and risks.

Suggested practice: How would you turn a business request into functional specifications?

What to coverDescribe how you would refine the request with stakeholders, document expected behavior and clarify open questions with engineering teams and data owners. Explain how you would keep the specifications tied to the business outcome during implementation.

Suggested practice: How would you handle an automation solution that fails during testing or early use?

What to coverWalk through how you would reproduce and characterize the issue, involve the appropriate business and technical partners, and decide whether to revise or pause the solution. Relate your approach to the SDLC and to communicating risks promptly.

Suggested practice: How would you determine whether a process-transformation project delivered value?

What to coverDescribe how you would establish a baseline and compare outcomes against the efficiency, scalability or risk-reduction goal. Explain how you would communicate both the result and its limitations to business and technical teams.

Behavioural practice

Suggested practice: Tell me about a time you learned an unfamiliar business domain and earned stakeholder trust.

What to coverUse a real situation, the actions you took to understand the work and build trust, and the result. Make clear how what you learned changed your approach.

Suggested practice: Tell me about a time stakeholders had competing priorities for technology work.

What to coverSet out the situation, how you compared needs and facilitated a decision, and the result. Explain how you communicated a trade-off to anyone whose request was deferred.

Suggested practice: Tell me about a time you translated business needs for an engineering or data team.

What to coverDescribe the situation, your actions to resolve ambiguity and align the teams, and the implementation result. Focus on your contribution rather than attributing the whole outcome to yourself.

Suggested practice: Tell me about a time you helped people adopt a new tool or process.

What to coverExplain the situation, what training or support you provided, and what happened after rollout. Include how you responded to feedback or signs that adoption was not working.

Suggested practice: Tell me about a time an emerging challenge put delivery at risk.

What to coverDescribe the situation, the practical steps you took and how you communicated the risk and progress. State the result, including any change to the plan.

02

Sharpen your knowledge

Topics to prepare

  • Process reengineering and risk

    Review how to distinguish a current-state process from a redesigned one, including bottlenecks, controls and scalability. Exercise: explain which steps in a hypothetical Shared Services process you would simplify, automate or retain. The posting explicitly calls for process optimization that improves efficiency and reduces risk.

  • Use-case prioritization and build versus buy

    Review how anticipated outcomes, complexity, urgency and business priorities affect decisions. Exercise: rank three hypothetical automation opportunities and explain when an existing tool might be preferable to building a solution. Both prioritization and build-versus-buy discussions are stated responsibilities.

  • LLMs, APIs and agentic workflows

    Review what each does, how they can connect, and where reliability or human review matters. Exercise: sketch a conceptual flow for an enterprise assistant handling a Shared Services request, including an API interaction and a review point. The posting explicitly seeks knowledge of LLMs, APIs and agentic workflows; the flow is a preparation example, not a specified project.

  • SDLC and robust AI delivery

    Review how requirements, implementation, testing and ongoing support fit together. Exercise: describe how you would check that a proposed AI solution works reliably and delivers a measurable business outcome. The posting asks for SDLC knowledge and experience implementing scalable, robust AI solutions that deliver business value.

  • Adoption and stakeholder alignment

    Review how business and technical audiences need different explanations of progress, risks and outcomes. Exercise: outline a short adoption plan covering stakeholder feedback, training and support. These activities reflect the posting's communication and change-management responsibilities.

Skills to revise

  • Map a Shared Services workflow

    Practice drawing a hypothetical current-state workflow with handoffs, delays and risk points, then annotate a proposed future state. This directly prepares for the posted process-mapping and reengineering work; the workflow is illustrative.

  • Triage automation opportunities

    Practice writing a one-page comparison of hypothetical opportunities, scoring expected outcomes, complexity and urgency, then defending the order to a business stakeholder. The role explicitly triages automation opportunities against those factors.

  • Build a small automation demonstration

    Practice implementing a limited, hypothetical rule-based workflow and explaining where an AI-assisted step could fit, including failure handling. The posting calls for hands-on AI and rule-based automation; this is a suggested exercise, not an employer-specified deliverable.

  • Write an engineering-ready functional specification

    Practice turning a hypothetical stakeholder request into a concise specification with workflow, inputs, outputs, exceptions and acceptance criteria; identify questions for engineering and data owners. The posting explicitly requires requirements refinement, functional specifications and collaboration on complex solutions.

  • Explain an enterprise-assistant rollout

    Practice a brief walkthrough of how you would introduce a Claude- or ChatGPT-like enterprise assistant to users, then prepare a short training explanation and a project update covering risks and outcomes. The posting mentions assistant integration, user training and communication; no specific rollout process is stated.

04

Your preparation checklist

  • Map a Shared Services workflow

    Prepare an illustrative current-state and improved-state process map, marking handoffs, delays, risk points and possible automation opportunities. The role explicitly calls for process mapping and reengineering.

  • Build a use-case prioritization sheet

    Rank a few illustrative AI, rule-based automation and process-change ideas by anticipated outcome, complexity and business urgency. Note where a build-versus-buy decision would be needed; these are stated responsibilities.

  • Prepare an AI implementation walkthrough

    If you have a relevant corporate implementation, outline the problem, your hands-on contribution, architecture, testing, rollout and measured business value. Be ready to explain how LLMs, APIs or agentic workflows were used, if applicable, and how the solution was made robust and scalable.

  • Draft a functional specification

    For one complex illustrative use case, write a short specification covering user needs, workflow, data inputs, acceptance criteria and open questions for engineering and data owners. The posting calls for translating business requirements into functional specifications.

  • Plan adoption and status communication

    Create a sample stakeholder update covering progress, risks, decisions and outcomes, plus a brief training and support plan for a new tool. The role includes communicating across business and technical teams and supporting adoption.

  • Prepare questions for the employer

    Ask which Shared Services processes are priorities, how opportunities are evaluated, when engineering support is engaged, and how success and adoption are measured. These questions follow from the advertised responsibilities; the posting does not specify the answers.

AI-generated from the job information · Prepared Sep 30, 2026 (UTC)Useful preparation, not a guarantee of eligibility or an offer.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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