Bees~1mo ago

Data Engineer | BEES Personalization

Brazil·Campinasmid

Data EngineeringData EngineerDataData & AI

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Quick Summary

Overview

About AB InBev AB InBev is the leading global brewer and one of the top 5 consumer goods companies in the world. With over 500 beer brands, we are the number one or two in many of the main beer markets worldwide, including North America, Latin America, Europe, Asia, and Africa.

Key Responsibilities

Implement ETL / ELT solutions and data integration between multiple systems and data sources. Design, implement, and maintain data pipelines to ingest, store, and process large volumes of data.

Requirements Summary

Develop and maintain APIs for data exchange between applications. Experience with cloud computing platforms (AWS, Azure , Google Cloud) and their services. Use automation frameworks and tools such as Azure DevOps, Terraform, and GitHub Actions.

Technical Tools

airflowawsazuregcpgithub-actionspythonscalasparksqlterraformb2bdatabase-designetl

Responsibilities

~1 min read

→Implement ETL/ELT solutions and data integration between multiple systems and data sources.
→Design, implement, and maintain data pipelines to ingest, store, and process large volumes of data.
→Collaborate with other teams to ensure data security and compliance.
→Design, develop, and implement solutions to optimize the performance and scalability of data processing systems.

Requirements

~1 min read

Bachelor's degree in Computer Science, Computer Engineering, Information Systems, Systems Analysis and Development, or similar;
Intermediate English;
Apply knowledge of data pipeline concepts and tools to implement data transformation, cleaning, and aggregation tasks.
Use data pipeline frameworks and libraries to automate data processing tasks and optimize workflows.
Follow best practices for developing data pipelines, including version control, testing, and thorough documentation.
Apply programming skills in Python, PySpark, Scala, and SQL to effectively manipulate and transform data.
Understand and utilize cloud computing platforms and services offered by providers such as AWS, Azure, and Google Cloud.
Develop data pipelines using orchestration frameworks (e.g., Apache Airflow, Luigi, Mage, Databricks Workflows), integrating with PySpark and/or Scala as needed.
Understand and apply software design principles to data engineering projects.
Apply data modeling techniques to design efficient and scalable data models.
Optimize PySpark and SQL query performance, considering data volume, query complexity, and system resources.
Use data tools for ingestion, transformation, analysis, and visualization.

Logical reasoning and analytical skills;
Meeting deadlines and delivering quality work;
Effective and transparent communication with other teams and coworkers;
Communication and interpersonal skills to present and discuss cases with other involved areas;
Autonomy in performing tasks;

Nice to Have

~1 min read

Develop and maintain APIs for data exchange between applications.
Experience with cloud computing platforms (AWS, Azure, Google Cloud) and their services.
Use automation frameworks and tools such as Azure DevOps, Terraform, and GitHub Actions.