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 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 aboard your authentic personality and experience 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.