Junior Data Engineer

MAIN RESPONSIBILITIES & SCOPE
Data Pipeline Development
 Build and maintain ETL / ELT data pipelines to ingest and transform data from internal and external source systems
 Support data ingestion using Microsoft Fabric, Azure Data Services, and cloud-based data platforms
 Implement data transformations using SQL, Python, notebooks, or Spark-based tools under guidance from senior engineers
 Assist with integrating structured and semi-structured data into the Lakehouse / Data Lake environment
Data Quality & Reliability
 Apply basic data quality checks and validation rules to ensure accuracy and consistency
 Monitor pipeline execution and help resolve data issues or failures
 Support root-cause analysis for data incidents and contribute to continuous improvement
Platform & Performance Support
 Assist in managing datasets in Azure Data Lake Storage (ADLS) and Microsoft Fabric Lakehouse
 Help optimize data models and queries for analytics and reporting use cases
 Learn and apply basic performance concepts such as partitioning and incremental loading
Collaboration & Documentation
 Work closely with senior data engineers, data analysts, and data scientists to understand data requirements
 Contribute to technical documentation, data models, and pipeline descriptions
 Follow established engineering standards, best practices, and governance guidelines
Learning & Development
 Actively learn modern data engineering tools, frameworks, and ways of working
 Participate in code reviews, team knowledge sharing, and community sessions
Scope:
 Supports multiple business functions and domains across Barry Callebaut
 Works within a global, distributed data and analytics organization

EDUCATION, LANGUAGE, SKILLS & QUALIFICATIONS
 Bachelor’s degree in Computer Science, Engineering, Data, Information

 Proficiency in English (written and spoken)

Section 2 - CANDIDATE PROFILE
ESSENTIAL EXPERIENCE & KNOWLEDGE / TECHNICAL OR FUNCTIONAL COMPETENCIES
 2–4years of experience (or strong academic / internship experience) in data engineering, analytics engineering, or software
engineering
 Basic understanding of data engineering concepts:
 ETL / ELT pipelines
 Data lakes and data warehouses
 Structured vs semi-structured data
 Hands-on experience with SQL
 Basic programming skills in Python (or willingness to learn quickly)
 Familiarity with cloud data platforms (Azure preferred) through coursework, projects, or internships
 Exposure to tools such as Microsoft Fabric, Azure Data Factory, Synapse, Databricks, or Spark is a plus (not mandatory)
 Understanding of version control concepts (e.g. Git) and collaborative development practices
PERSONAL ATTRIBUTES AND WAYS OF WORKING
The ideal candidate…
 Curious and eager to learn new technologies and data concepts
 Structured and detail-oriented approach to problem solving
 Comfortable asking questions and learning from more experienced team members
 Collaborative team player who communicates clearly with technical and non-technical stakeholders
 Takes ownership of assigned tasks and follows through reliably
ADDITIONAL COMMENTS / CONTEXT OF THE ROLE
 This role is part of the Barry Callebaut Data & Analytics organization, led by the CDAO
 The position offers strong learning opportunities in cloud data platforms, analytics, and AI-ready data foundations
 Clear progression paths toward Data Engineer, Analytics Engineer, or Platform Engineer roles