Consultant, Data Engineering

The Coca-Cola Company; Dublin; Presencial; EUR 75 000 - 120 000; Tempo integral

Resumo da oferta

The Coca-Cola Company seeks a data and AI product professional to translate business needs into AI-enabled data solutions. You will work across Business, Product Management, Data Science, Data Engineering, Architecture and AI teams to ensure trusted data and real-user value.

Responsibilities include shaping practical AI use cases, defining requirements, and guiding delivery with engineering logic. You will enable adoption, track outcomes and drive measurable impact for users.

Qualificações

• Approximately five years of relevant experience in business analysis, data and analytics, AI use-case development, product delivery or technology-enabled transformation.
• Experience working between business users and technical teams to gather requirements and define measurable outcomes.
• Experience using data/AI tools to support decisions, processes or productivity.
• Understanding of product management, Agile delivery, data lifecycles and responsible AI concepts.

Responsabilidades

• Translate business needs into AI and data use cases with clear requirements and delivery plans.
• Shape high-value AI and data use cases grounded in real workflows and user needs.
• Provide engineering logic and delivery clarity to ensure testable and explainable solutions.
• Enable AI adoption and value realisation by embedding solutions into day-to-day decisions.
• Connect business users with AI/engineering teams, translating language and surfacing risks early.

Conhecimentos

AI Use-Case Definition

Data Analytics & Insight

Engineering Logic & Problem Solving

Requirements & User Stories

Agile Ways of Working

Business Partnering

AI Use-Case Development

Formação académica

Bachelor's degree in Business Administration, Information Systems, Data Analytics, Computer Science, Engineering, Finance, Economics, or related

Academic or professional experience combining business, data and tech

Certifications in Business Analysis, Product Management, AI, Cloud

Advanced degree in Business Analytics, Data Science, AI (advantageous)

Combination of education, certifications and experience considered

Ferramentas

Data governance

Descrição da oferta de emprego

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 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.

4. Enable AI Adoption and Value Realisation

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

What success looks like

• 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.

5. Connect Business Users with AI and Engineering Teams

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

What success looks like

• 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.

Education Qualification

• 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.

Skills and Experience You Need

• 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.

Core Skills

• 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

We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state or local protected class.