Senior Manager Data Engineer

About BEES

Join us to build the future of B2B commerce!
BEES is AB InBev’s B2B platform. Through our ecosystem, merchants and retailers across 29 countries can stock their businesses quickly, easily, and securely. At BEES, we dream big, lead with purpose, and develop technology that transforms the way retailers and sellers grow.
Every line of code and every partnership is built in service of a single mission: to make commerce better for retailers and sellers around the world. Here, your work is not just important. It makes a difference!

• →You will lead a team of data engineers to deliver world-class safety solutions
• →Partner with customer teams, help see opportunities, gather requirements, and lead a technology roadmap and vision for the team
• →Recruit, encourage, and grow a top-notch engineering team
• →Develop systems and software to bring world class safety to global BEES

• Degree in Computer Science, Computer Engineering, Information Systems, Systems Development Analysis or similar;
• Advanced English;
• Design and develop data pipeline architectures for advanced analytics, machine learning, and real-time data processing.
• Innovate techniques to optimize data pipeline performance, efficiency, and reliability.
• Understand cloud computing platforms and services offered by providers like AWS, Azure, and Google Cloud.
• Analyze design requirements and constraints to identify optimal solutions for data and software architecture.
• Design scalable, reliable, and performant systems that meet business needs based on architecture requirements.
• Apply data quality assessment techniques such as data profiling, cleansing, and validation to identify and correct data quality issues.
• Implement monitoring and logging architectures for visibility and analysis of system performance and security.
• Analyze CI/CD pipeline configurations and logs for issue diagnosis and optimization opportunities.

• Degree in Computer Science, Computer Engineering, Information Systems, Systems Development Analysis or similar;
• Advanced English;
• Design and develop data pipeline architectures for advanced analytics, machine learning, and real-time data processing.
• Innovate techniques to optimize data pipeline performance, efficiency, and reliability.
• Understand cloud computing platforms and services offered by providers like AWS, Azure, and Google Cloud.
• Analyze design requirements and constraints to identify optimal solutions for data and software architecture.
• Design scalable, reliable, and performant systems that meet business needs based on architecture requirements.
• Apply data quality assessment techniques such as data profiling, cleansing, and validation to identify and correct data quality issues.
• Implement monitoring and logging architectures for visibility and analysis of system performance and security.
• Analyze CI/CD pipeline configurations and logs for issue diagnosis and optimization opportunities.
• Python, SQL proficiency
• Test automation and quality engineering
• Version control and Git workflows
• Design patterns and reusable frameworks
• Batch and streaming architectures
• Data modeling (dimensional, Data Vault, semantic layers)
• Enterprise-scale ETL/ELT design
• Metadata and lineage management

• Hiring and talent development
• Coaching and mentoring engineers
• Set engineering standards and best practices
• Drive architecture decisions
• Build a culture of engineering excellence

• Significant experience building and operating critical high-scale systems
• Good software engineering methodology: meaningful and deeply rooted opinions about testing and code quality, ability to make sound quality/speed trade-offs
• Understand principles and assumptions of different machine learning capabilities.