AB InBev is hiring a Mid-Level Data Engineer

What You 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. Requirements and Qualifications - 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. Security, Compliance & Governance - 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 organization. How You Work - Demonstrate curiosity and a continuous learning mindset. - Write clean, readable, and maintainable code. - Follow coding standards, testing practices, and development workflows. - Communicate progress, blockers, and technical questions clearly. - Participate in code reviews and knowledge-sharing activities. - Take ownership of assigned tasks while escalating risks or uncertainties appropriately. - Contribute positively to team collaboration and a culture of continuous improvement. Nice to Have - Experience with Databricks, dbt, or similar technologies. - Familiarity with CI/CD tools such as GitHub Actions, Azure DevOps - Familiarity with APIs, JSON, event-driven architectures, or messaging systems. - Exposure to vulnerability scanning, secret scanning, or secure development practices. - Understanding of privacy regulations such as LGPD, GDPR, or similar frameworks.