Senior Manager Data Engineer

AB InBev is the leading global brewer and one of the world’s top 5 consumer product companies. With over 500 beer brands, we’re number one or two in many of the world’s top beer markets, including North America, Latin America, Europe, Asia, and Africa.

Created in 2022, the Growth Group unifies our business-to-business (B2B), direct-to-consumer (DTC), Sales & Distribution, and Marketing teams. By bringing together global tech and commercial functions, the Growth Group allows us to fully leverage data and drive digital transformation and organic growth for AB InBev around the world.

In addition to supporting well known global beer brands like Corona, Budweiser and Michelob Ultra, the Growth Group is home to a robust suite of digital products including our B2B digital commerce platform BEES, on-demand delivery services Ze Delivery and TaDa Delivery, and table top beer keg PerfectDraft.

We are an exceptional team, focused on understanding and supporting consumer and customer needs, harnessing new technology, and scaling growth opportunities.

• →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.

AB InBev Growth Group is proud to be an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon of race, color, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other applicable legally protected characteristics.

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Remember: your data will never be used as elimination criteria in selection processes. With them, AB InBev Growth Group is able to analyze diversity and reduce biases in selection processes. We want to contribute to changing this reality by being an inclusive company. 

For more information: www.abinbev.com