Data Engineering Consultant

The Coca-Cola Company

. Translate business needs into clear AI and data use cases, requirements and delivery plans .

Posted 10/8/2026full-timeSofia • BulgariaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in translating business needs into AI and data use cases, while effectively collaborating with technical teams to ensure successful product delivery and user adoption. Proficient in assessing data quality, governance, and implementing process improvements to drive measurable outcomes.

Highest-signal resume keywords

Business AnalysisAI Use-Case DevelopmentData AnalyticsAgile DeliveryProcess Improvement

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data ModellingData IntegrationData GovernanceUser Journey MappingRequirements GatheringAcceptance Criteria DefinitionTechnical Feasibility AssessmentBusiness Process OptimisationAI ConceptsChange Management

Soft Skills

Critical ThinkingProblem SolvingFacilitationCommunicationRelationship Building

Tools & Technologies

AI-Enabled ToolsData Analytics ToolsProduct Management SoftwareAgile ToolsCollaboration Platforms

Certifications & Qualifications

Business Analysis CertificationProduct Management CertificationAgile CertificationData Analytics CertificationChange Management Certification

Industry Keywords

Data LifecycleGenerative AIMachine LearningResponsible AI PrinciplesUser Productivity

Tech Stack

Tools & technologies

Cloud

About the role

Key responsibilities & impact

Translate business needs into clear AI and data use cases, requirements and delivery plans
Document business questions, user needs, pain points, problem statements, user stories, process flows, acceptance criteria and measurable outcomes
Shape practical AI and data opportunities grounded in business decisions, workflows and user needs
Assess value, data readiness, technical feasibility, adoption needs, risk and strategic alignment
Apply engineering thinking to make solutions testable, explainable, usable and supportable
Define user journeys, decision logic, data inputs and outputs, business rules, exceptions and success measures
Consider data availability, quality, lineage, access, privacy and dependencies; document and escalate gaps
Work with engineers, data scientists and architects to clarify requirements, test assumptions and refine options
Support testing, validation and acceptance of solutions
Help users adopt AI-enabled data products in day-to-day decisions, processes and ways of working
Identify trust, explainability, responsible use, training and change needs
Track usage, feedback, outcomes, benefits and adoption to support continuous improvement
Connect business users with Product, Data Science, Data Engineering, Architecture and AI teams
Surface dependencies, assumptions, risks and trade-offs and support resolution
Maintain alignment on user problems, outcomes, scope, ownership and success measures throughout discovery, build, launch and optimisation

Requirements

What you’ll need

Bachelor's degree in Business Administration, Information Systems, Data Analytics, Computer Science, Engineering, Finance, Economics, Supply Chain or a related discipline
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
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 trust, explainability, privacy and human oversight
Ability to build effective working relationships across business, Data & Analytics, Product, Architecture, Engineering and AI teams
Critical-thinking, problem-solving, facilitation and communication skills
Relevant learning or certifications in Business Analysis, Product Management, Agile, Data Analytics, AI, Cloud, Change Management or Continuous Improvement are beneficial
Advanced degree is advantageous but not required

Benefits

Comp & perks

Travel required: 00%–25%
Relocation provided: No
Inclusive growth culture
Continuous learning opportunities