Job Opportunities API

About us 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. About BEES At BEES, 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. What you'll do: Support the development and maintenance of data pipelines, ingestion processes, and data transformations. Create and maintain SQL queries, Python scripts, and Spark-based workloads used for data processing and analytics. Assist in troubleshooting pipeline failures, data quality issues, and operational incidents. Work with senior engineers to implement schema mappings, transformation logic, and data validation rules. Ensure datasets meet expected schemas, data contracts, and quality standards. Support metadata management, dataset documentation, and lineage activities. Assist in maintaining data classification information according to company standards. Help automate repetitive operational and data management tasks to improve efficiency and reliability. Contribute to monitoring, alerting, and operational support for data pipelines and workflows. Participate in testing activities, including unit tests, transformation validation, and data quality checks. Follow established engineering standards, coding practices, and team development patterns. Learn and apply security, privacy, and compliance requirements when handling sensitive or regulated data. Collaborate with Data Governance, Security, and Compliance teams when required. Contribute to continuous improvement initiatives focused on data trust, reliability, and operational excellence.   What you'll need: Bachelor's degree in Computer Science, Computer Engineering, Information Systems, Data Science, Software Engineering, or related fields. Basic to intermediate English. Up to 2 years of experience in Data Engineering, Software Engineering, Data Analytics, or related areas. Knowledge of SQL and Python. Understanding of ETL/ELT concepts and data transformation processes. Familiarity with relational databases and data warehousing concepts. Basic knowledge of Spark, Databricks, or distributed data processing frameworks. Familiarity with Git and version control workflows. Basic understanding of cloud platforms such as AWS, Azure, or Google Cloud. Knowledge of automation concepts and scripting for operational efficiency. Basic understanding of data quality concepts and validation practices. Familiarity with data governance principles, including metadata, ownership, stewardship, and documentation. Basic knowledge of data classification concepts (Public, Internal, Confidential, Restricted). Understanding of data lineage and traceability concepts. Awareness of security best practices, including access management, secrets management, and least-privilege principles. Strong analytical, problem-solving, and communication skills. Willingness to learn new technologies and collaborate across teams. Follow company standards for handling sensitive and regulated data. Apply data classification requirements when creating or maintaining datasets and pipelines. Use approved authentication, authorization, and secrets management mechanisms. Avoid exposing sensitive information through logs, exports, testing data, or documentation. Support auditability by maintaining documentation, metadata, and lineage information. Escalate security, privacy, or compliance concerns when requirements are unclear. Follow established governance processes and contribute to improving data trust across the or