Senior Google Cloud Data Engineer

New

BrazilFull-TimeSenior

Salary not disclosed

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Job Details

Strong hands-on experience in data engineering using Python for automation, pipeline development, and data processing.
Advanced SQL expertise, including complex queries, nested structures, and analytical functions.
Deep experience with Google Cloud Platform, especially BigQuery, Dataflow (Apache Beam), and Pub/Sub.
Proven ability to build scalable batch and streaming pipelines with high reliability and performance.
Strong understanding of data modeling, transformation frameworks, and modern data architecture principles.
Experience implementing dbt workflows, CI/CD pipelines, and data governance practices.
Expertise in Looker, including LookML, semantic modeling, explores, and dashboard development for diverse audiences.
Strong knowledge of data quality frameworks, monitoring systems, and production-grade data operations.
Experience optimizing cloud cost and performance in large-scale distributed data environments.
Ability to work independently while collaborating effectively in agile, cross-functional teams.
Strong communication skills with the ability to translate technical concepts into business insights.
Advanced English proficiency.

Design, build, and maintain scalable batch and real-time data pipelines using GCP services such as BigQuery, Dataflow, and Pub/Sub.
Develop robust ETL/ELT workflows, ensuring high availability, fault tolerance, and data accuracy across distributed systems.
Implement and maintain dbt transformation models, CI/CD pipelines, and structured data contracts for curated datasets and analytical marts.
Optimize BigQuery performance through advanced query tuning, partitioning, clustering, and cost-efficient architecture design.
Build and monitor data quality frameworks using tools such as Great Expectations, including freshness SLOs and reconciliation checks.
Develop event-driven architectures and streaming pipelines using windowing, triggers, and watermarking strategies.
Design and maintain LookML semantic models, ensuring consistent metrics and governance across the organization.
Build impactful dashboards in Looker for both operational monitoring and executive reporting use cases.
Perform root cause analysis to resolve data, pipeline, or performance issues and implement permanent fixes.
Implement platform reliability controls including retries, dead-letter queues, disaster recovery runbooks, and security validation.
Collaborate with cross-functional teams to ensure alignment between data engineering, analytics, and business needs.
Document systems, pipelines, and architectural decisions to ensure transparency and maintainability.

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