Senior Data Engineer

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. What you'll do: Own the technical design and delivery of the data model for a BEES business domain. Start from business intent and from the data sources that exist, and produce authoritative definitions for entities, assets, attributes, relationships, identifiers, and grain. Profile source systems and Unity Catalog tables (grain, keys, cardinality, overlap, nulls, and how columns are derived) and decide which sources can support a trustworthy representation of the business. Produce and evolve the domain's canonical logical model and canonical datasets, including identifiers, relationships, and cardinalities. Map attributes to physical columns, with transformation, nullability, and lineage documented so consumers can trust the result. Design master-data outcomes for the domain, including entity resolution: match approach, blocking keys, duplicate handling, cross-system identity, and survivorship when sources disagree. Define advanced data-quality expectations on the model (uniqueness at the declared grain, referential integrity, completeness, validity, consistency across sources, reconciliation, and freshness) and contribute to the Data Quality Framework and Tooling so those expectations become executable checks and monitoring. Shape the model for its consumers. Data scientists and data analysts are the primary consumers. The same model feeds downstream data products such as curated tables, metric views, and feature tables, with a stable grain and documented semantics. Contribute to the ontology codebase: Python packages, SQL and Spark on Databricks, YAML definition contracts, entity classification, lineage-aware mappings, and tests. Review pull requests on models, mappings, and ontology-aligned dataset changes. Drive entity and attribute lifecycle work: creation, change, split, rename, deprecation, and retirement, with compatibility guidance and clear communication to consumers. Escalate cross-domain conflicts or ownership disputes when broader alignment is required. Mentor engineers on the team through design review, source-analysis pairing, and hands-on support What you'll need: Senior experience as a data engineer, data analyst, or any equivalent role in data, who has designed conceptual and logical data models from operational source systems in a complex business. A record of turning ambiguous business context and imperfect data into a model with explicit entities, grain, keys, relationships, and cardinalities that downstream teams actually use. Hands-on master data management, including entity resolution: deterministic and probabilistic matching, blocking, duplicate detection, golden-record or survivorship rules, and identity across systems. Advanced data quality technique. You have designed and applied uniqueness, referential integrity, validity, completeness, consistency, reconciliation, and timeliness checks, and you have seen them catch real defects before consumers did. SQL, Python, or a quality framework in production counts. Traditional data engineering: production SQL (joins, aggregations, window functions), batch ELT patterns, and enough PySpark or Spark SQL to profile a source and prototype a conformed dataset on Databricks. Ability to contribute to a Python codebase: change an existing package, add a test, and carry a pull request through review.