Senior Manager Data Engineer

1 мес. назад

БразилияSeniorC1

awsazuregoogle clouddata pipelinemachine learningreal-time data processing

Lead a team of data engineers to develop world-class data pipeline architectures and safety solutions at platform.

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.
At , our ambition is – and always will be – to put customers at the heart of everything we do, making their lives easier and their businesses more profitable. Through our B2B e-commerce and SaaS platform, we bring the power of digital to small and medium-sized retailers, unlocking new growth opportunities for all.

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

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.