Senior Data Engineer | Bees Personalization

AB InBev is the leading global brewer and one of the world’s top 5 consumer product companies. They are seeking a Senior Data Engineer to lead the development and maintenance of data services and solutions, design efficient data models, and improve data product performance for the BEES technology cell.

Responsibilities:

• Leads efforts within the organization to drive the development and maintenance of data services and solutions to support products, downstream services, or infrastructure tools and platforms used across BEES
• Design efficient data models and understand concepts such as normalization, denormalization, and dimensional modeling
• Implement improvements in architecture and processes to improve the performance, monitoring, and evolution of data products
• Develop and maintain data ingestion, processing, control/security, and data provisioning processes for different consumers (services, front-end, among others)
• Contribute to obtaining knowledge in the business context and creating new data products to meet their strategic and operational needs

Requirements:

• Degree in Computer Science, Computer Engineering, Information Systems, Systems Development Analysis or similar
• Advanced English
• Assess scalability, reliability, security, and compliance implications of data pipeline designs
• Understand cloud computing platforms and services offered by providers like AWS, Azure, and Google Cloud
• Evaluate programming solutions in Python, PySpark, Scala, and SQL for data processing and analysis
• Evaluate data quality processes and controls to ensure accuracy and completeness
• Apply debugging techniques to identify and resolve cross-module issues
• Implement monitoring, alerting, and failure handling mechanisms in architecture designs
• Assess effectiveness of CI/CD principles and practices in automated pipelines
• Design, develop, and maintain APIs for data exchange between applications
• Apply Agile practices to plan, execute, and deliver data engineering tasks and projects
• Logical reasoning and analytical skills
• Meeting deadlines and quality of work
• Effective and transparent dialog with other areas and co-workers
• Ability to communicate and interpersonal relationships to present cases and discuss them with other areas involved
• Being independent in activities
• Work as part of a team, promoting a good relationship with the team
• Evaluate data pipeline solutions for latency, throughput and accuracy
• Analyze data requirements and business needs to inform data modeling decisions
• Understand the principles and assumptions of different machine learning capabilities
• Integrate data sets with visualization tools to create insightful reports and dashboards