Engenheiro de Dados
New
BrazilFull-TimeMiddle
Salary not disclosed
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Job Details
- Required Skills
- AWSSQLAgileGCPGitMicrosoft AzureAirflowCI/CDData modeling
Requirements
- Solid experience in Data Engineering, with hands-on expertise in building and maintaining end-to-end data pipelines.
- Advanced proficiency in SQL, including the ability to create complex and high-performance queries.
- Strong knowledge of data modeling principles and best practices for analytical and operational environments.
- Experience with orchestration tools such as Airflow, Prefect, Dagster, or similar technologies.
- Familiarity with Delta Lake and data versioning strategies for historical data management.
- Practical experience with cloud platforms such as AWS, Google Cloud Platform (GCP), or Microsoft Azure.
- Knowledge of data security practices, including governance, anonymization, masking, and compliance requirements.
- Experience with Git, CI/CD practices, and agile methodologies.
- Strong communication and stakeholder management skills, with the ability to work effectively in cross-functional teams.
Responsibilities
- Design, build, and maintain scalable and resilient data pipelines for ingestion, transformation, and orchestration of critical business data.
- Collaborate with engineering teams, external data providers, and business stakeholders to understand requirements and translate them into efficient data solutions.
- Develop and optimize data models to improve storage efficiency, query performance, and data accessibility.
- Implement and promote engineering best practices, including code versioning, automated testing, metadata documentation, and data quality validation.
- Ensure data consistency, reliability, and governance through robust monitoring, security controls, and lifecycle management processes.
- Act as a bridge between engineering, analytics, data science, and business teams, fostering collaboration and enabling data-driven initiatives.
- Document processes, workflows, and technical solutions to facilitate transparency and knowledge sharing across teams.
- Identify opportunities for continuous improvement in platform architecture, scalability, and operational efficiency.
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