Consultant, Data Engineering

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. 

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. 

• 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. 

• 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. 

• 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 non-functional requirements, test assumptions and refine options. 

• Supports testing, validation and acceptance to confirm that solutions meet business needs and operate as intended. 

• Understands enough of data models, APIs, integration, analytics and AI solution patterns to engage technical teams effectively without being the hands-on technical lead. 

• Current workflows, decision points and pain points are mapped and translated into practical future-state experiences. 

• AI and data experiences are designed to improve efficiency, consistency, user experience and decision quality. 

• Trust, explainability, responsible use, training and change needs are identified and addressed alongside delivery. 

• Usage, feedback and outcome measures are used to refine products, prompts, workflows and priorities. 

• Benefits and adoption are tracked against agreed measures to demonstrate value and inform continuous improvement. 

• Trusted relationships are built with business users, Product Managers, Data Scientists, Data Engineers, Architects and AI specialists. 

• Business language is translated into delivery language, and technical choices, constraints and limitations are explained clearly to non-technical stakeholders. 

• Dependencies, assumptions, risks and trade-offs are surfaced early and supported towards constructive resolution. 

• Teams remain aligned on the user problem, intended outcome, delivery scope, ownership and measures of success. 

• Feedback flows continuously between users and delivery teams throughout discovery, build, launch and optimisation. 

Use business insight, available data and an AI-first mindset to identify and shape practical opportunities that improve decisions, productivity and business performance. 

Apply structured engineering thinking to help delivery teams move from a business problem to a solution that is testable, explainable, usable and supportable. 

Help users adopt AI-enabled data products and embed them into day-to-day decisions, processes and ways of working. 

Act as a practical connector between front-facing business teams and the technical teams that design, build and run data and AI solutions. 

• Bachelor's degree in Business Administration, Information Systems, Data Analytics, Computer Science, Engineering, Finance, Economics, Supply Chain or a related discipline. 

• Academic or professional experience combining business, data and technology disciplines is highly desirable. 

• Relevant learning or certifications in Business Analysis, Product Management, Agile, Data Analytics, AI, Cloud, Change Management or Continuous Improvement are beneficial. 

• An advanced degree in Business Analytics, Information Management, Data Science, AI or a related field is advantageous but not required. 

• A combination of education, relevant certifications and equivalent professional experience may be considered in place of specific degree requirements. 

• Approximately five years of relevant experience in business analysis, data and analytics, AI use-case development, product delivery, process improvement or technology-enabled transformation. Equivalent experience gained through related roles or assignments may be considered. 

• Practical experience working between business users and technical teams to gather requirements, shape AI or data use cases, document business and data logic, support delivery and define measurable outcomes. 

• Experience using data, analytics or AI-enabled tools to support analysis, decisions, process improvement or user productivity. 

• Understanding of product management, Agile delivery, data lifecycles, AI concepts, business process optimisation, organisational change and value realisation. 

• Working knowledge of data availability, quality, modelling, integration and governance concepts. 

• Awareness of generative AI, machine learning and responsible AI principles, including the importance of trust, explainability, privacy and appropriate human oversight. 

• Ability to build effective working relationships across business, Data & Analytics, Product, Architecture, Engineering and AI teams. 

• Sound critical-thinking, problem-solving, facilitation and communication skills, with a willingness to learn and develop. 

• AI and Data Business Analysis 

• Business Partnering 

• AI Use-Case Definition 

• Data Analytics & Insight 

• Engineering Logic & Problem Solving 

• Requirements and User Stories 

• Data and AI Product Delivery 

• Agile Ways of Working 

• Business Process Design 

• Adoption and Value Realisation 

• Business and Technical Communication 

• Responsible AI Awareness 

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We are taking deliberate action to nurture an inclusive culture that is grounded in our company purpose, to refresh the world and make a difference. We act with a growth mindset, take an expansive approach to what’s possible and believe in continuous learning to improve our business and ourselves. We focus on four key behaviors – curious, empowered, inclusive and agile – and value how we work as much as what we achieve. We believe that our culture is one of the reasons our company continues to thrive after 130+ years. Visit Our Purpose and Vision to learn more about these behaviors and how you can bring them to life in your next role at Coca-Cola.