Senior Google Cloud Data Engineer

This position is posted by Jobgether on behalf of a partner company.

We are currently looking for a Senior Google Cloud Data Engineer in Brazil.

This role is focused on designing, building, and optimizing modern, scalable data platforms on Google Cloud Platform to support high-impact digital and analytics products.

You will work on both real-time and batch data pipelines that process large-scale, complex datasets powering business-critical insights.

The position involves deep collaboration with analytics, data science, and engineering teams in a global, agile environment.

You will play a key role in ensuring data reliability, performance, governance, and cost efficiency across cloud-based architectures.

Beyond pipeline development, you will contribute to data modeling, transformation standards, and BI enablement using Looker and BigQuery.

This is a hands-on technical role with strong ownership, combining engineering excellence with strategic data platform thinking.

AccountabilitiesDesign, 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.

RequirementsStrong 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.

BenefitsCompetitive compensation aligned with market standards and experience level.

Flexible remote work model within Brazil.

Opportunity to work with cutting-edge Google Cloud technologies and large-scale data systems.

International, collaborative environment with strong engineering culture.

Career growth opportunities in data engineering, cloud architecture, and analytics leadership.

Exposure to global clients and complex, high-impact data challenges.

Continuous learning culture with support for certifications and technical development.

How Jobgether WorksWe use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements.

Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company.

The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best! Why Apply Through Jobgether?Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer.

This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR).

You may exercise your rights (access, rectification, erasure, objection) at any time.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses.

These tools assist our recruitment team but do not replace human judgment.

Final hiring decisions are ultimately made by humans.

If you would like more information about how your data is processed, please contact us.