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. Our stack is Python 3.11+, Databricks (Unity Catalog and Delta), SQL, and YAML contracts in Git.
- Experience with catalog or metadata systems, mapping business attributes onto physical tables and columns, lineage, and data contracts between producers and consumers.
- Experience reviewing other engineers' models and code, and mentoring through design feedback and documentation.
- Collaboration in GitHub or Azure DevOps, including review of changes from outside the immediate team.
- Clear written and verbal communication with business stakeholders, data scientists, and data analysts. Definitions should be usable without a meeting.
- Bachelor's degree in computer science, engineering, information systems, or another quantitative field, or equivalent practical experience.
Nice to have:
- Relevant data management and governance experience: business glossaries, critical data elements, stewardship, naming standards, and dataset certification. The team will teach the ontology vocabulary.
- Prior work on Azure Databricks or Unity Catalog.
- Experience preparing data for data scientists or analysts: feature grain, metric grain, slowly changing attributes, or point-in-time correctness.
- Exposure to entity classification, schema matching, or duplication discovery across a large table estate.
- Prior experience in a marketplace, B2B, or B2C company.
More about you:
- You own the model for your domain after it ships: you answer consumer questions, and you change the definition when the data or the business shows it is wrong.
- You trust a model after you have looked at the data, and you can show the grain, the key, and the source behind a definition.
- You care that a data scientist or a data analyst can use an entity without reverse-engineering the source system.
- You treat identifiers, match rules, and grain as product decisions, because a wrong key breaks quality, metrics, and downstream products together.
- You write definitions, mappings, and quality expectations that another engineer can implement.
- You raise the bar through review, documentation, and practical mentorship.
- You balance delivery urgency with compatibility when an entity that consumers already use has to change.
What we offer
- • Performance-based bonus*
• Attendance bonus*
• Private pension plan
• Meal allowance
• Casual office and dress code
• Days off*
• Health, dental, and life insurance plans
• Discounts on medications
• Partnership with WellHub
• Childcare assistance
• Discounts on Ambev products*
• Clube Ben partnership
• Scholarship program*
• School supplies support
• Language learning platforms and training
• Transportation allowance
*Rules applied
Equal Opportunity & Affirmative Action:
AB InBev Growth Group is proud to be an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon of race, color, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other applicable legally protected characteristics.
The following fields are optional, but anticipate the information for your registration*.
Remember: your data will never be used as elimination criteria in selection processes. With them, AB InBev Growth Group is able to analyze diversity and reduce biases in selection processes. We want to contribute to changing this reality by being an inclusive company.
For more information: www.abinbev.com
Skills Required
- Senior experience as a data engineer, data analyst, or equivalent data role in a complex business
- Experience designing conceptual and logical data models from operational source systems
- Experience defining entities, grain, keys, relationships, and cardinalities
- Hands-on master data management experience, including entity resolution, matching, duplicate detection, golden records, survivorship rules, and cross-system identity
- Advanced data-quality experience covering uniqueness, referential integrity, validity, completeness, consistency, reconciliation, and timeliness checks
- Production SQL experience with joins, aggregations, and window functions
- Experience with batch ELT patterns and PySpark or Spark SQL
- Experience using Databricks, Unity Catalog, Delta, SQL, Python, and YAML contracts in Git
- Ability to contribute to Python codebases, write tests, and complete pull requests through review
- Experience with catalog or metadata systems, physical table and column mappings, lineage, and data contracts
- Experience reviewing engineering models and code and mentoring engineers
- Experience collaborating through GitHub or Azure DevOps
- Clear written and verbal communication with business stakeholders, data scientists, and data analysts
- Bachelor's degree in computer science, engineering, information systems, quantitative field, or equivalent practical experience
- Data management and governance experience, including business glossaries, critical data elements, stewardship, naming standards, and dataset certification
- Prior experience with Azure Databricks or Unity Catalog
- Experience preparing data for data scientists or analysts, including feature grain, metric grain, slowly changing attributes, or point-in-time correctness
- Experience with entity classification, schema matching, or duplication discovery across large table estates
- Prior experience in a marketplace, B2B, or B2C company
What We Do
We are the world’s leading brewer bringing people together for a better world. For centuries, the experience of sharing a beer has brought people and cultures together. Even in our hyper-connected, always-on world, this simple act is as meaningful today as it was generations ago. We are AB InBev. Committed to driving growth that leads to better living for more people in more places. Through brands and experiences that bring people together. Through our dedication to brewing the best beer with the best ingredients. And through our commitment to helping farmers, retailers, entrepreneurs, and communities grow. We are building a company to last. Not just for a decade. But for the next 100 years. Through our brands and our investment in communities, we will bring more people together, making our company an integral part of our consumers’ lives for generations to come. Our diverse portfolio of well over 500 beer brands includes global brands Budweiser, Corona and Stella Artois; multi-country brands Beck’s, Castle, Castle Light, Leffe and Hoegaarden; and local champions such as Aguila, Antarctica, Bud Light, Brahma, Cass, Chernigivske, Cristal, Harbin, Jupiler, Klinskoye, Michelob Ultra, Modelo Especial, Quilmes, Victoria, Sedrin, Sibirskaya Korona, and Skol. Anheuser-Busch InBev is a publicly traded company (Euronext: ABI) based in Leuven, Belgium, with secondary listings on the Mexico (MEXBOL: ANB) and South Africa (JSE: ANH) stock exchanges and with American Depositary Receipts on the New York Stock Exchange (NYSE: BUD).
