Mid-Level Data Engineer

This is a fully remote position, open to applicants in Brazil.

📋 Description

• Provide assistance in the creation and upkeep of data pipelines, ingestion procedures, and data transformations.

• Develop and sustain SQL queries, Python scripts, and Spark-based tasks utilized for data processing and analytics.

• Aid in diagnosing pipeline failures, data quality challenges, and operational incidents.

• Collaborate with senior engineers to execute schema mappings, transformation logic, and data validation protocols.

• Confirm that datasets adhere to expected schemas, data contracts, and quality benchmarks.

• Assist in the management of metadata, dataset documentation, and lineage activities.

• Support the maintenance of data classification details in alignment with company standards.

• Help streamline repetitive operational and data management tasks to enhance efficiency and reliability.

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

• Engage in testing activities, including unit tests, transformation validation, and data quality assessments.

• Adhere to established engineering standards, coding practices, and team development methodologies.

• Learn and implement security, privacy, and compliance requirements when managing sensitive or regulated data.

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

• Participate in continuous improvement initiatives aimed at enhancing data trust, reliability, and operational excellence.

⛳️ Requirements

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

• Basic to intermediate proficiency in English.

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

• Proficiency in SQL and Python.

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

• Familiarity with relational databases and data warehousing principles.

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

• Familiarity with Git and version control practices.

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

• Knowledge of automation concepts and scripting for operational efficiency.

• Basic comprehension of data quality principles and validation practices.

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

• Basic knowledge of data classification principles (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 abilities.

• Willingness to learn new technologies and collaborate across teams.

🏝️ Benefits

• Health insurance

• Paid time off

• Professional development

• Home office setup