Consultant, Data Engineering

Job Description Summary: Role Purpose  Work at the intersection of business needs, data, AI and engineering. Partner with business teams to understand decisions, pain points and opportunities, then translate them into clear use cases, requirements and delivery inputs for AI-enabled data products and experiences.  Work across Product Management, Data Science, Data Engineering, Architecture and AI teams to ensure solutions remain grounded in trusted data and real user needs. Bring enough engineering logic to structure problems, understand data and technical dependencies, and support delivery, while remaining focused on business engagement, adoption and measurable value.  Succes is measured the quality of business problems translated into practical AI and data use cases, the clarity of requirements and engineering logic provided to delivery teams, and the adoption and measurable value created for users.  Key Accountabilities  1. Translate Business Needs into AI and Data Solutions  Work with business users to understand the decisions they need to make and translate those needs into clear AI and data use cases, requirements and delivery plans.  What success looks like  Business questions, user needs, pain points and desired decisions are understood and documented.  Needs are translated into clear problem statements, user stories, process flows, acceptance criteria and measurable outcomes.  Business, Product, Data Science, Data Engineering, Architecture and AI teams have shared clarity on users, outcomes, scope, data needs and dependencies.  AI and data product roadmaps remain connected to growth, productivity, efficiency, decision quality and user outcomes.  2. Shape High-Value AI and Data Use Cases  Use business insight, available data and an AI-first mindset to identify and shape practical opportunities that improve decisions, productivity and business performance.  What success looks like  Use cases are grounded in real business decisions, workflows and user needs rather than technology-led concepts.  Opportunities are supported by relevant data, evidence, user insight and baseline performance measures.  Structured analysis helps teams assess value, data readiness, technical feasibility, adoption needs, risk and strategic alignment.  AI capabilities are translated into responsible, practical and scalable business applications.  3. Provide Engineering Logic and Delivery Clarity  Apply structured engineering thinking to help delivery teams move from a business problem to a solution that is testable, explainable, usable and supportable .  What success looks like  Requirements describe the user journey, decision logic, data inputs and outputs, business rules, exceptions and success measures.  Data availability, quality, lineage, access, privacy and dependencies are considered early, with gaps clearly documented and escalated.  Works with engineers, data scientists and architects to clarify functional and…