Senior Data Engineer, AWS

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

📋 Description

• Design, develop, and maintain scalable data pipelines for enterprise analytics and reporting.

• Create batch and near-real-time data ingestion processes from various sources.

• Construct and enhance ETL/ELT solutions.

• Implement and oversee Data Lake and Data Warehouse architectures within cloud environments.

• Develop data transformations utilizing Databricks, Apache Spark, and PySpark.

• Establish integrations with AWS Glue, Lambda, and S3.

• Ensure data quality, consistency, traceability, and governance.

• Monitor, troubleshoot, and enhance the performance and reliability of pipelines.

• Collaborate with BI teams to support analytics solutions and enterprise reporting.

• Develop and refine models and integrations for Qlik Sense and QlikView.

• Work alongside architects, data analysts, BI developers, and business stakeholders.

• Engage in code reviews, architecture discussions, and continuous improvement efforts.

• Document processes, data models, integration flows, and operational procedures.

⛳️ Requirements

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related discipline.

• Over 5 years of professional experience in the industry.

• Proficient in developing pipelines, ETL/ELT processes, and contemporary Data Lake and Data Warehouse architectures.

• Strong expertise in Databricks and Apache Spark (PySpark).

• Advanced programming abilities in Python and SQL.

• Experience in data modeling.

• Practical experience with AWS Glue, AWS Lambda, Amazon S3, and AWS IAM.

• Background in Business Intelligence and Analytics projects.

• Experience in integrating and modeling data for Qlik Sense/QlikView.

• Familiarity with Git and CI/CD practices.

• Proficient in English for effective communication and collaboration with diverse teams.

🏝️ Benefits

• Flexible remote work environment.

• Opportunity to interact with individuals from around the globe in a multicultural setting.

• Chances to build skills and expertise in data, AI, and decision-making.

• Opportunities for collaboration, knowledge sharing, and continuous improvement.