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 AB InBev Growth Group 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. What you do Implement and maintain individual components of the data platform—for example, ingestion jobs, dbt models, Spark transformations, CDC tasks, matching rules, or deduplication logic. Make implementation decisions within a component : schema mapping, transformation logic, join strategy, and similar choices bounded to that unit of work. Fix defects in transformations, ingestion jobs, or entity resolution logic when issues are identified. Ensure component outputs match the expected schema, data contracts, and downstream expectations. Improve a component’s performance, data quality checks, or reliability when gaps or incidents require it. Follow existing ETL and MDM standards and team patterns rather than inventing parallel approaches. Apply security and compliance expectations to your components: handle sensitive and personal data according to classification, retention, and minimization rules; avoid logging, samples, or exports that over-collect or expose regulated fields beyond what the use case requires. Use approved identity, access, and secrets patterns for jobs and services (for example, role-based access, managed identities, or vault-backed credentials)— not hard-coded secrets or ad hoc shared accounts. Support auditability of changes and data movement as the team defines it (for example, clear job ownership, metadata, lineage hooks, or evidence packs for controls) so security and compliance reviews can trace what the pipeline does. Requirements and qualifications Bachelor's degree in Computer Science, Computer Engineering, Information Systems, Systems Analysis and Development, or similar. Intermediate English. Code quality: write clear, readable, modular code; follow team naming and formatting conventions; avoid unnecessary duplication in your own changes; prefer changes that can be understood without a verbal walkthrough. Verification: add required unit or transformation-level tests; validate schema assumptions and basic data quality conditions; ensure changes do not break existing behavior. Delivery: submit well-structured pull requests that include a clear description of the change, context, and expected impact, and evidence of testing. Stack (typical): Python, SQL, and data processing with PySpark and/or Scala as used in the team’s pipelines. Pipelines: practical experience building or maintaining batch/stream components with orchestration (for example, Apache Airflow, Databricks Workflows, or similar) and version control (Git). Data work: comfortable with transformation, cleansing, aggregation, and basic performance tuning for SQL and Spark workloads, given volume and complexity. Cloud: familiarity with services on a major provider (AWS, Azure, or Google Cloud) in the way the team deploys and runs jobs. Securit