Engenheiro de Dados Pleno GCP/DBT
New
BrazilFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- Minimum of 3 years
- Required Skills
- PythonSQLAgileETLGCPGitData modelingBigQuerydbtPySpark
Requirements
- Minimum of 3 years of hands-on experience working with DBT in production environments.
- Strong expertise in DBT concepts, including models, ref(), source(), macros (Jinja), seeds, snapshots, and testing frameworks.
- Solid understanding of layered data architecture approaches such as Staging, Transformation, and Data Mart/Data Warehouse models.
- Advanced experience with Google Cloud Platform services, especially BigQuery, Cloud Storage, Dataproc, Dataflow, Composer, and IAM.
- Strong knowledge of data modeling, query optimization, partitioning, clustering, data ingestion, governance, and security best practices within BigQuery.
- Proficiency in Python, PySpark, and advanced SQL for data engineering, transformation, automation, and integration tasks.
- Familiarity with Shell Scripting and automation processes.
- Experience with Git-based version control systems, including GitHub and Bitbucket.
- Understanding of networking, cloud security, VPCs, firewall configurations, and access management principles.
- Experience working within Agile environments and using Jira.
Responsibilities
- Analyze existing data warehouse architectures, data sources, and business requirements to define scalable cloud data solutions.
- Design and implement data architectures using Google Cloud Platform services, including data storage, processing, orchestration, and analytics components.
- Develop, maintain, and optimize ELT/ETL pipelines using DBT, BigQuery, Dataproc, Dataflow, and related technologies.
- Create efficient and scalable data models, including staging, transformation, and data mart layers aligned with modern data warehouse best practices.
- Define and execute data migration strategies, including full loads, incremental loads, and change data capture approaches.
- Implement data validation, monitoring, and quality controls to ensure reliability and consistency across data pipelines.
- Optimize query performance, resource utilization, and cloud infrastructure costs while maintaining scalability and operational efficiency.
- Apply data governance, security, and access control policies to ensure compliance and protect sensitive information.
- Troubleshoot and resolve performance, operational, and data-related issues across cloud environments and pipelines.
- Produce and maintain comprehensive technical documentation covering architecture, processes, pipelines, and operational procedures.
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