# Consultant, Data Engineering at Coca-Cola

Coca-Cola is hiring a Consultant, Data Engineering in Sofia, Bulgaria. Level rates it Works on AI ●●●○; you can [apply on Level](https://jobsbylevel.com/go/d3a5a90d-4808-4b11-a350-5f82f2f2b0a5).

AI Level 3, AI centrality 71 out of 100. Bulgaria - Sofia.

## Details

- Company: [Coca-Cola](https://jobsbylevel.com/companies/coca-cola)
- AI level: AI Level 3 (score 71 out of 100)
- Location: Bulgaria - Sofia
- Posted: October 8, 2026
- Apply: https://jobsbylevel.com/go/d3a5a90d-4808-4b11-a350-5f82f2f2b0a5

## Description

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 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

The description is cut here. Read the full offer: https://jobsbylevel.com/jobs/consultant-data-engineering-at-coca-cola-eace95

Source: https://jobsbylevel.com/jobs/consultant-data-engineering-at-coca-cola-eace95

## Cite this page

Level. https://jobsbylevel.com/jobs/consultant-data-engineering-at-coca-cola-eace95.

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