Intermediate Data Engineer

What You Do

• Support the development and maintenance of data pipelines, ingestion processes, and data transformations.

• Create and maintain SQL queries, Python scripts, and Spark-based workloads used for data processing and analytics.

• Assist in troubleshooting pipeline failures, data quality issues, and operational incidents.

• Work with senior engineers to implement schema mappings, transformation logic, and data validation rules.

• Ensure datasets meet expected schemas, data contracts, and quality standards.

• Support metadata management, dataset documentation, and lineage activities.

• Assist in maintaining data classification information according to company standards.

• Help automate repetitive operational and data management tasks to improve efficiency and reliability.

• Contribute to monitoring, alerting, and operational support for data pipelines and workflows.

• Participate in testing activities, including unit tests, transformation validation, and data quality checks.

• Follow established engineering standards, coding practices, and team development patterns.

• Learn and apply security, privacy, and compliance requirements when handling sensitive or regulated data.

• Collaborate with Data Governance, Security, and Compliance teams when required.

• Contribute to continuous improvement initiatives focused on data trust, reliability, and operational excellence.

Requirements and Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Information Systems, Data Science, Software Engineering, or related fields.

• Basic to intermediate English.

• Up to 2 years of experience in Data Engineering, Software Engineering, Data Analytics, or related areas.

• Knowledge of SQL and Python.

• Understanding of ETL/ELT concepts and data transformation processes.

• Familiarity with relational databases and data warehousing concepts.

• Basic knowledge of Spark, Databricks, or distributed data processing frameworks.

• Familiarity with Git and version control workflows.

• Basic understanding of cloud platforms such as AWS, Azure, or Google Cloud.

• Knowledge of automation concepts and scripting for operational efficiency.

• Basic understanding of data quality concepts and validation practices.

• Familiarity with data governance principles, including metadata, ownership, stewardship, and documentation.

• Basic knowledge of data classification concepts (Public, Internal, Confidential, Restricted).

• Understanding of data lineage and traceability concepts.

• Awareness of security best practices, including access management, secrets management, and least-privilege principles.

• Strong analytical, problem-solving, and communication skills.

• Willingness to learn new technologies and collaborate across teams.

Security, Compliance & Governance

• Follow company standards for handling sensitive and regulated data.

• Apply data classification requirements when creating or maintaining datasets and pipelines.

• Use approved authentication, authorization, and secrets management mechanisms.

• Avoid exposing sensitive information through logs, exports, testing data, or documentation.

• Support auditability by maintaining documentation, metadata, and lineage information.

• Escalate security, privacy, or compliance concerns when requirements are unclear.

• Follow established governance processes and contribute to improving data trust across the organization.

How You Work

• Demonstrate curiosity and a continuous learning mindset.

• Write clean, readable, and maintainable code.

• Follow coding standards, testing practices, and development workflows.

• Communicate progress, blockers, and technical questions clearly.

• Participate in code reviews and knowledge-sharing activities.

• Take ownership of assigned tasks while escalating risks or uncertainties appropriately.

• Contribute positively to team collaboration and a culture of continuous improvement.

Nice to Have

• Experience with Databricks, dbt, or similar technologies.

• Familiarity with CI/CD tools such as GitHub Actions, Azure DevOps

• Familiarity with APIs, JSON, event-driven architectures, or messaging systems.

• Exposure to vulnerability scanning, secret scanning, or secure development practices.

• Understanding of privacy regulations such as LGPD, GDPR, or similar frameworks.