Mid-Level Data Engineer

• →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.