Mid-Level Data Engineer

Requirements

• 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.

Additional Information

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.

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Company Intel

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