Descrição da Vaga

AWS DATA ENGINEER (HYBRID / REMOTE BRAZIL)

Brazilian company hires for hybrid or remote position

📍 Location: Brazil (any location)

⚠️ Only candidates already based in Brazil will be considered

💼 Work Model: Hybrid for candidates living in state capitals and Remote for candidates living in countryside/cities outside the state capitals

🗣️ Language Requirements: English C2 (Advanced/Fluent) – Mandatory (there will be direct contact with an international client), Native portuguese

🕓 Seniority: Senior (6+ years)

💰 Compensation: Please inform your salary expectations when applying.

⚠️ Instructions: Please send your CV in English and make sure to include all skills and experience that match the requirements of the opportunity. This will significantly increase your chances of success.

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Build Scalable Cloud Data Platforms on AWS

We are looking for an experienced AWS Data Engineer to design, develop, and optimize cloud-native data platforms that support enterprise analytics and business intelligence initiatives.

In this role, you will build scalable ETL pipelines, optimize distributed data processing, and collaborate with Data Engineers, Data Scientists, and business stakeholders to deliver reliable, high-performance data solutions on AWS.

If you are passionate about cloud data engineering and modern data architectures, this opportunity is for you.

The Professional We Are Looking For

We are seeking a highly technical Data Engineer with solid expertise in AWS data services and modern data engineering practices.

The ideal candidate has hands-on experience with AWS Glue, Apache Spark, Amazon Redshift, Python or Scala, and data warehousing concepts. You should be comfortable designing scalable cloud solutions, solving complex data challenges, and collaborating with international teams.

Key Responsibilities

You will be responsible for:

• Designing, developing, and implementing AWS data solutions using AWS Glue, Apache Spark, and Amazon Redshift.
• Collaborating with Data Engineers and Data Scientists to understand business requirements and translate them into scalable data pipelines.
• Building and managing ETL processes using AWS Glue to extract, transform, and load data from multiple sources.
• Optimizing distributed data processing workflows using Apache Spark to ensure efficient data processing and analysis.
• Developing and maintaining data models and schemas within Amazon Redshift to support reporting and analytics.
• Monitoring, troubleshooting, and optimizing data pipelines to ensure data quality, reliability, and integrity.
• Documenting data processes, architecture, and engineering best practices to support knowledge sharing and compliance.

Mandatory Requirements (Eliminatory)

⚠️ All requirements below are mandatory and eliminatory. Candidates who cannot clearly demonstrate these qualifications in their CV are unlikely to proceed in the recruitment process.

Education

✔ Bachelor's Degree in:

• Information Systems
• Computer Science
• Data Engineering
• or a related field

Mandatory Experience

• English C2 (Advanced/Fluent) with the ability to communicate confidently in international environments.
• Proven experience developing AWS cloud data solutions.
• Strong hands-on experience with: AWS Glue Apache Spark Amazon Redshift
• AWS Glue
• Apache Spark
• Amazon Redshift
• Strong programming skills using Python or Scala.
• Solid understanding of Data Warehousing concepts and best practices.
• Strong knowledge of SQL.
• Experience with relational database management systems.
• Experience designing and implementing ETL pipelines.
• Strong analytical thinking and problem-solving skills.

Nice-to-Have Skills

The following qualifications will be considered a strong advantage:

• Experience with enterprise-scale cloud data platforms.
• Data Lake and Lakehouse architectures.
• Infrastructure as Code (IaC).
• CI/CD pipelines for Data Engineering.
• Data governance and data quality initiatives.
• Experience working with international teams.
• Experience within the Financial Services industry.

What You'll Find in This Opportunity

• Enterprise-scale AWS cloud projects.
• Modern Data Engineering environment.
• Collaboration with Data Engineers and Data Scientists.
• Exposure to large-scale distributed data processing.
• Opportunities to design scalable cloud-native data architectures.
• International, collaborative, and innovation-driven environment.

Before Applying, Ask Yourself These 5 Questions

✅ Do I have proven hands-on experience with AWS Glue, Apache Spark, and Amazon Redshift in production environments?

✅ Does my CV clearly demonstrate experience designing cloud data solutions and building scalable ETL pipelines?

✅ Am I proficient in Python or Scala, as well as advanced SQL for Data Engineering projects?

✅ Do I have a solid understanding of Data Warehousing concepts and experience working with relational databases?

✅ Am I fluent in English (C2) and comfortable collaborating with international teams and stakeholders?

If you answered "No" to one or more of these questions, we recommend carefully reviewing your fit before applying.

⚠️ Important

This position is intended for an AWS Data Engineer with strong hands-on experience building cloud-native data platforms.

Candidates whose experience is primarily focused on Business Intelligence, Reporting, Data Analysis, or Software Development, without significant practical experience with AWS Glue, Apache Spark, Amazon Redshift, ETL pipelines, and cloud data engineering, are unlikely to match the expectations for this role.

Keywords That Should Appear in Your CV

AWS Data Engineer, Data Engineer, Amazon Web Services, AWS, AWS Glue, Apache Spark, Spark, Amazon Redshift, Redshift, ETL, ETL Pipelines, Data Pipelines, Data Engineering, Cloud Data Engineering, Python, Scala, SQL, Data Warehousing, Data Warehouse, Data Modeling, Cloud Data Platform, AWS Cloud, Distributed Data Processing, Big Data, Data Integration, Data Transformation, Data Quality, Relational Databases, Analytics, Business Intelligence, Data Architecture, Performance Optimization, Enterprise Data Solutions.

#EY BR