<p><span style="font-size: 12pt;"><strong>About BEES</strong></span></p> <p>Join us to build the future of B2B commerce!<br>BEES is AB InBev’s B2B platform. Through our ecosystem, merchants and retailers across 29 countries can stock their businesses quickly, easily, and securely. At BEES, we dream big, lead with purpose, and develop technology that transforms the way retailers and sellers grow.<br>Every line of code and every partnership is built in service of a single mission: to make commerce better for retailers and sellers around the world. Here, your work is not just important. It makes a difference!</p> <p><strong>What you'll do:</strong></p> <ul> <li>Implement and maintain <strong>individual components</strong> of the data platform—for example, ingestion jobs, dbt models, Spark transformations, CDC tasks, matching rules, or deduplication logic.</li> <li>Make <strong>implementation decisions within a component</strong>: schema mapping, transformation logic, join strategy, and similar choices bounded to that unit of work.</li> <li><strong>Fix defects</strong> in transformations, ingestion jobs, or entity resolution logic when issues are identified.</li> <li>Ensure <strong>component outputs match</strong> the expected schema, data contracts, and downstream expectations.</li> <li><strong>Improve</strong> a component’s performance, data quality checks, or reliability when gaps or incidents require it.</li> <li><strong>Follow existing ETL and MDM standards</strong> and team patterns rather than inventing parallel approaches.</li> <li><strong>Apply security and compliance expectations</strong> to your components: handle sensitive and personal data according to <strong>classification, retention, and minimization</strong> rules; avoid logging, samples, or exports that <strong>over-collect or expose</strong> regulated fields beyond what the use case requires.</li> <li><strong>Use approved identity, access, and secrets patterns</strong> for jobs and services (for example, role-based access, managed identities, or vault-backed credentials)—<strong>not</strong> hard-coded secrets or ad hoc shared accounts.</li> <li><strong>Support auditability</strong> 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.</li> </ul> <p><span data-teams="true"><strong>What you'll need:</strong></span></p> <ul> <li>Bachelor's degree in Computer Science, Computer Engineering, Information Systems, Systems Analysis and Development, or similar.</li> <li>Intermediate English.</li> <li><strong>Code quality:</strong> 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.</li> <li><strong>Verification:</strong> add required unit or transformation-level tests; validate schema assumptions and basic data quality conditions; ensure changes do not break existing behavior.</li> <li><strong>Delivery:</strong> submit well-structured pull requests that include a clear description of the change, context, and expected impact, and evidence of testing.</li> <li><strong>Stack (typical):</strong> Python, SQL, and data processing with <strong>PySpark and/or Scala</strong> as used in the team’s pipelines.</li> <li><strong>Pipelines:</strong> practical experience building or maintaining batch/stream components with orchestration (for example, Apache Airflow, Databricks Workflows, or similar) and version control (Git).</li> <li><strong>Data work:</strong> comfortable with transformation, cleansing, aggregation, and basic performance tuning for SQL and Spark workloads, given volume and complexity.</li> <li><strong>Cloud:</strong> familiarity with services on a major provider (AWS, Azure, or Google Cloud) in the way the team deploys and runs jobs.</li> <li><strong>Security baseline for data engineering:</strong> follow <strong>least-privilege</strong> IAM and service principals for pipelines; prefer <strong>encryption in transit and at rest</strong> where the platform provides it; keep dependencies and images <strong>within approved channels</strong> and address <strong>high-severity</strong> findings from scanners or security tooling when they affect your components.</li> <li><strong>Compliance-aware delivery:</strong> When a change touches regulated data, new integrations, or new exports, <strong>document data purpose, flows, and safeguards</strong> in the PR or linked ticket so risk and compliance partners can assess impact without guesswork.</li> </ul> <p><span data-teams="true"><strong>More about you:</strong></span></p> <ul> <li>Hands-on with transformation tooling and data contracts in a shared warehouse.</li> <li>APIs or event interfaces used for data exchange between systems.</li> <li><strong>Infrastructure-as-code</strong> or CI/CD (for example, Azure DevOps, Terraform, GitHub Actions) for job deployment.</li> <li>Familiarity with <strong>data governance</strong> tooling (catalog, quality, policy tags) or <strong>vuln