Senior Data Engineer Campinas, São Paulo, Brazil

BEES; Campinas; On-site; BRL 140,000 - 190,000; Full time

Benefits offered by this job

Performance-based bonus

Attendance bonus

Casual office and dress code

Days off

Health, dental, and life insurance

Discounts on medications

WellHub partnership

Discounts on Ambev products

Language learning platforms and培训

Job summary

BEES is seeking a data engineer to implement and maintain data platform components, including ingestion jobs, Spark transformations, and CDC tasks. You will define schema mappings, transformation logic, and ensure outputs meet data contracts.

You will optimize performance, follow ETL and MDM standards, apply security and compliance rules, and collaborate with teams deploying on cloud platforms using Python, SQL, PySpark, and Scala.

Qualifications

• Bachelor's degree or equivalent in a relevant field.
• Proven experience delivering data engineering solutions.
• Strong understanding of data contracts and schema validations.
• Experience with PySpark/Scala and cloud-based data platforms.
• Ability to write clean, testable code and PR-driven delivery.

Responsibilities

• Implement and maintain components of the data platform (ingestion jobs, dbt models, Spark transformations, CDC tasks).
• Make component-level implementation decisions (schema mapping, transformations, join strategies).
• Fix defects in transformations, ingestion jobs, or entity resolution logic.
• Ensure component outputs align with data contracts and downstream expectations.
• Improve performance, data quality checks, and reliability; follow ETL/MDM standards.
• Apply security/compliance requirements and use approved IAM patterns.

Skills

Code quality

Testing

Delivery discipline

Security awareness

Data governance knowledge

Education

Bachelor's degree in Computer Science, Computer Engineering, Information Systems, Systems Analysis and Development, or similar

Tools

Airflow

Databricks

Git

Terraform

GitHub Actions

Job description

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 BEES

At BEES, our ambition is – and always will be – to put customers at the heart of everything we do, making their lives easier and their businesses more profitable. Through our B2B e-commerce and SaaS platform, we bring the power of digital to small and medium-sized retailers, unlocking new growth opportunities for all.

What you'll 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.

What you'll need:

• Bachelor's degree in Computer Science, Computer Engineering, Information Systems, Systems Analysis and Development, or similar.
• 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.
• Security baseline for data engineering: follow least-privilege IAM and service principals for pipelines; prefer encryption in transit and at rest where the platform provides it; keep dependencies and images within approved channels and address high-severity findings from scanners or security tooling when they affect your components.
• Compliance-aware delivery: When a change touches regulated data, new integrations, or new exports, document data purpose, flows, and safeguards in the PR or linked ticket so risk and compliance partners can assess impact without guesswork.

More about you:

• Hands-on with transformation tooling and data contracts in a shared warehouse.
• APIs or event interfaces used for data exchange between systems.
• Infrastructure-as-code or CI/CD (for example, Azure DevOps, Terraform, GitHub Actions) for job deployment.
• Familiarity withdata governance tooling (catalog, quality, policy tags) or vulnerability / secret scanning in CI for data repos and pipelines.

What we offer

• Performance-based bonus*
• Attendance bonus*
• Casual office and dress code
• Days off*
• Health, dental, and life insurance plans
• Discounts on medications
• Partnership with WellHub
• Discounts on Ambev products*
• Language learning platforms and training

Equal Opportunity & Affirmitive Action:

AB InBev Growth Group is proud to be an Equal Opportunity and Affirmitive 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.