- At CI&T, we help large enterprises transform the potential of AI into real business impact with AI Deployment, AI-native execution, and tech-integrated business solutions.
- With 30 years of experience in technological transformation, we accelerate innovation with expertise in Agentic SDLC, Application modernization, Data & AI, Martech and Business strategy.
- We are 8,000 CI&Ters across more than 25 countries, collaborating to build solutions with real impact. AI is already part of how we work, evolve, and innovate every day.
** Must have good English => C1 or above
At CI&T, we're growing fast and looking for a talented, motivated Data Engineer to join our team. You'll play a crucial role in designing, developing, and maintaining scalable data pipelines and infrastructure to drive data analytics and machine learning solutions for our global clients.
Key Responsabilities
- Design, develop, and maintain scalable data pipelines and infrastructure.
- Collaborate with cross-functional teams to ensure data integrity and usability.
- Document data workflows, processes, and architectures.
- Integrate data quality into data pipelines.
- Develop CI/CD pipelines for ETL processes.
- Implement dimensional data modeling.
- Implement Data Lake/Data Warehouse solutions.
- Implement Data Governance practices (catalog, lineage, etc).
- Provide technical support and troubleshooting for data-related issues.
Qualifications
- Good English communication skills is mandatory (reading, writing, and speaking).
- Proven experience working with modern data engineering stacks in the cloud.
- Experience with the Databricks platform is a must-have for this position.
- Strong knowledge of Spark, Python, and SQL.
- Knowledge of CI/CD pipelines for ETL processes.
- Expertise in dimensional data modeling.
- Experience with Data Lake/Data Warehouse implementations.
Nice to Have
- Databricks certification is a plus.
- Experience working with international clients.
- Experience working with Azure and AWS.
- Experience with Power BI.
- Familiarity with Data Quality practices.
- Familiarity with Data Governance using Unity Catalog.
- Knowledge of FinOps as applied to the data space.
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