Data Engineer at GBfoods

Job description

At GBfoods, we offer culinary solutions in several countries of Europe and Africa with prestigious leading local brands, including Jumbo, Gallina Blanca, Erasco, Gino, Liebig, Star, Bama, D&L, Grand'Italia, and Blå Band, among others.  GBfoods Africa operates in 24 countries with a Regional office in Accra, Ghana.

Our Purpose “Celebrating local flavours” is to empower and take care of each of the local communities we belong to, and to bring out their authentic flavours. Some of our local brands have been in consumers' kitchens for over a century and have positioned themselves as authentically loved brands, as well as deeply rooted parts of the local culture.

Today, with a turnover of around 1,5 billion euros and a team of around 3,600 people, we are the preferred choice of millions of consumers.

 Role:

Reporting to the Commercial Data Manager,

The Data Engineer will be responsible for designing, building, and managing scalable data pipelines and services that support strategic business units across the company. The role ensures data is integrated, reliable, and governed, enabling analytics, BI, and AI initiatives.

Working cross-functionally with technical teams and business stakeholders, the Data Engineer will help create a consistent, business-focused data foundation that drives decision-making, innovation, and advanced analytics at a Group level.

 Key Responsibilities:

Group Data Engineering & Integration

• Design, build, and maintain end-to-end data pipelines that ingest, transform, and serve data from multiple internal and external sources across the Group.
• Contribute to the development and operation of Group data lake and data warehouse layers, ensuring consistency, scalability, and reusability across SBUs.
• Integrate heterogeneous data domains (e.g., commercial, marketing, consumer, financial, operational data) into a coherent and well-structured data environment.

Data Modelling & Business Enablement

• Design and maintain data models and semantic layers that translate raw data into business-ready structures for analytics, BI, and AI use cases.
• Work closely with analytics and business teams to understand key questions and translate them into robust data structures.
• Ensure that data is accessible, understandable, and usable by downstream consumers.

Data Governance & Quality

• Implement and enforce Group data governance standards, including: Data cataloguing and documentation; Lineage and traceability; Access control and security rules; PII handling and masking policies.
• Proactively monitor and improve data quality, reliability, and freshness, ensuring trust in Group data assets.

Operations & Continuous Improvement

• Monitor data pipelines, identify issues proactively, and ensure reliable and performant data delivery.
• Apply modern data engineering best practices such as version control, automated testing, monitoring, and documentation.
• Contribute to the continuous improvement of Group data standards, patterns, and tooling, in collaboration with Data Architecture and IT teams.

AI-Enabled & Future-Oriented Ways of Working

• Leverage AI-assisted development tools (e.g., LLMs, automation, intelligent documentation) to accelerate development, improve quality, and increase efficiency.
• Actively explore new approaches to data engineering that align with the Group’s mid- to long-term digital and AI ambitions.

 What can you expect from us?

You will be part of a unique company in an exciting moment, when we are growing and becoming a very consolidated multinational in the FMCG Food industry, with many strong brands.

 What do we expect from you?

We expect that you will bring your authentic personality and experience on board to effectively develop and implement strategies to drive the growth of our existing business.

Requirements

• Minimum bachelor’s degree in Computer Science, Software Engineering, Data Science, or related field.
• At least 5 years of experience in data engineering, data warehousing, or BI-related roles.
• Strong proficiency in Python and SQL.
• Hands-on experience with cloud data platforms (e.g., Azure data services, Snowflake, Big Query, or similar).
• Solid understanding of Data modelling; ETL/ELT pipelines; Data warehouse and Lakehouse concepts.
• Experience with workflow orchestration tools (e.g., Apache Airflow or equivalent).
• Familiarity with Version control and collaborative development (Git); Basic CI/CD principles for data pipelines, data quality, and validation concepts.
• Exposure to distributed processing technologies (e.g., Spark, Databricks) is a plus.
• Ability to translate business requirements into data solutions.
• Strong collaboration skills in cross-functional, international environments.
• Clear communication skills, able to explain technical topics to non-technical stakeholders.
• Ownership mindset and sense of responsibility for data products.
• Curiosity and continuous-learning attitude, especially towards AI-native and modern data engineering practices.
• Results-oriented, pragmatic, and focused on business impact.