Data Engineer

Calyptus; São Paulo; Presencial; BRL 80.000 - 120.000; Tempo integral

Resumo da oferta

A tech company in São Paulo is looking for a Data Engineer with over 3 years of experience to design and build data pipelines for AI processing. The ideal candidate should be proficient in Python and SQL, have experience with data integration tools, and a strong understanding of data privacy practices. This role offers the opportunity to collaborate with data scientists and engineers to optimize data workflows and ensure compliance with governance requirements.

Qualificações

• Minimum 3 years of experience with data engineering in enterprise environments.
• Strong proficiency in Python and SQL for data processing.
• Experience with ETL/ELT tools and data integration patterns.

Responsabilidades

• Design and build data pipelines for AI processing.
• Develop connectors for enterprise systems.
• Implement data quality frameworks for AI models.

Conhecimentos

Data engineering experience

Proficiency in Python

Proficiency in SQL

ETL / ELT tools experience

Knowledge of APIs

Understanding of data privacy best practices

Cloud platforms experience

Containerization tools familiarity

Formação académica

Bachelor's degree in Computer Science

Descrição da oferta de emprego

You’re a perfect match if you have :

• 3+ years experience with data engineering in enterprise environments
• Strong proficiency in Python and SQL for data processing and transformation
• Experience with ETL / ELT tools and data integration patterns
• Knowledge of enterprise data standards and APIs (REST, GraphQL, SOAP)
• Understanding of data privacy, security, and access control best practices
• Experience with cloud platforms (AWS, Azure, GCP) and data infrastructure
• Familiarity with containerization and orchestration tools
• Bachelor's degree in Computer Science, Engineering, Data Science, or related field

Your day-to-day activities :

• Design and build data pipelines that integrate diverse enterprise data sources for AI processing
• Develop connectors and adapters for enterprise systems (CRM, ERP, document management, databases)
• Implement data quality frameworks and validation systems for AI model inputs
• Create secure data handling workflows that support global development teams
• Build data transformation pipelines that prepare unstructured and structured data for AI models
• Establish data governance and lineage tracking for compliance and audit requirements
• Optimize data processing workflows for scale and performance
• Collaborate with data scientists and AI engineers to ensure data meets model requirements