Engenheiro de Dados Pleno
J
JobgetherData Engineering
BrazilFull-TimeMiddle
Salary not disclosed
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Job Details
- Required Skills
- AWSPostgreSQLSQLGCPGitAzure
Requirements
- Professional experience working as a Data Engineer or in a comparable data engineering role.
- Advanced knowledge of SQL, with the ability to develop complex queries and perform data extraction, transformation, analysis, and optimization.
- Hands-on experience developing ETL/ELT processes and data pipelines.
- Experience working with relational databases such as SQL Server, PostgreSQL, Oracle, or MySQL.
- Experience processing and manipulating large volumes of data in production or enterprise environments.
- Knowledge of Git and collaborative version-control platforms such as GitHub or GitLab.
- Understanding of Data Lake, Data Warehouse, and Lakehouse concepts and architectures.
- Professional experience with cloud environments, preferably AWS, Azure, or Google Cloud Platform (GCP).
- Strong analytical and problem-solving skills, with the ability to investigate data issues and develop practical technical solutions.
- Good communication and collaboration skills, with the ability to work effectively across technical, analytical, and business teams.
Responsibilities
- Develop, maintain, and continuously improve data pipelines and ETL/ELT processes, ensuring reliable ingestion, transformation, and delivery of data.
- Build and maintain integrations between data sources and platforms, supporting the availability and consistency of information used by business and analytical teams.
- Work with relational databases and large datasets to process, transform, organize, and optimize data according to business and technical requirements.
- Contribute to data architecture initiatives involving Data Lakes, Data Warehouses, and Lakehouse environments.
- Monitor and troubleshoot data pipelines and processes, identifying issues and implementing solutions to maintain data quality, availability, security, and reliability.
- Collaborate with Engineering, Analytics, Data Science, BI, and business stakeholders to understand requirements and translate them into effective data solutions.
- Apply development and version-control best practices using Git-based tools and contribute to reliable, maintainable data engineering workflows.
- Support the implementation and evolution of data solutions in cloud environments, adapting to the organization’s infrastructure and technology requirements.
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