Pessoa Especialista em Engenharia de Dados Sênior
New
J
JobgetherData engineering
Based in Brazil; Freedom to work from anywhere in Brazil.Full-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- PythonSQLMicrosoft AzureSparkScalaData modeling
Requirements
- Advanced proficiency in SQL for manipulating, transforming, and analyzing large volumes of data.
- Strong professional experience with Python or Scala for data engineering, pipeline development, and automation.
- Hands-on experience with Apache Spark and distributed data processing technologies.
- Deep knowledge of Data Lake, Data Warehouse, and Lakehouse architecture design, implementation, and optimization.
- Extensive experience developing, maintaining, and optimizing ETL/ELT pipelines in cloud-based environments.
- Advanced knowledge of data modeling, data governance, and data quality management.
- Practical experience with Microsoft Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP).
- Understanding of data integration patterns, pipeline orchestration, and reliable data processing workflows.
- Ability to define technical standards and evaluate architectural alternatives across performance, scalability, security, and cost.
- Strong analytical and problem-solving skills for complex data engineering challenges.
- Ability to collaborate with multidisciplinary teams and align technical solutions with business objectives.
- Autonomy and commitment to continuous improvement, technical excellence, and knowledge sharing.
Responsibilities
- Design, build, and maintain data ingestion, transformation, and integration pipelines.
- Develop and evolve Data Lake, Data Warehouse, and Lakehouse architectures.
- Implement and optimize ETL/ELT processes.
- Establish data quality, governance, security, and management standards.
- Integrate data sources and destinations across platforms and systems.
- Apply distributed processing technologies to large datasets and analytical workloads.
- Define technical standards, architectural guidelines, and engineering best practices.
- Monitor, troubleshoot, and optimize data pipelines and platform components.
- Collaborate with multidisciplinary teams to translate business requirements into data solutions.
- Improve data infrastructure, architectures, and engineering processes.
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