Want to know if this job is worth applying to?
Brasil
Remote only
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Google Cloud Lead Data Engineer based in Brazil.
This is a senior, hands-on data engineering role focused on building trusted marketing and product data warehouses in Google Cloud.
You’ll lead data engineering work across client engagements, from raw data ingestion through production-ready models and reporting layers.
The role combines Python, BigQuery, Dataform, Cloud Run, IAM, and Google Cloud operations in real-world environments.
You’ll translate analytics requirements into reliable data models and help clients understand how their data supports business decisions.
Working alongside project managers, strategists, engineers, and client stakeholders, you’ll balance technical quality with fixed budgets and delivery timelines.
You’ll also mentor engineers and establish documentation, monitoring, and operational practices that make solutions sustainable after handover.
The position is fully remote across Latin America, with collaboration alongside US-based teams during normal working hours.
Lead data engineering for client engagements, designing data architectures that bring GA4, marketing platforms, CMS content, and other systems into BigQuery.
Own the full data lifecycle, from raw landing tables and ingestion through Dataform models, analytical views, and dashboard-ready datasets.
Build and operate Dataform projects, including assertions, scheduled runs, development-to-production promotion, and data quality controls.
Develop and maintain Python ingestion services on Cloud Run, including API integrations, pagination, rate limits, watermarks, backfills, and recovery mechanisms.
Configure and support the cloud infrastructure required for reliable warehouse operations, including service accounts, IAM, monitoring, cost controls, and operational runbooks.
Troubleshoot discrepancies between source systems, warehouse models, and reporting dashboards, identifying and resolving issues at the data-model level.
Translate reporting and business requirements into robust data solutions, including mapping GA4 events and parameters to relevant KPIs.
Collaborate with project managers and strategists by providing realistic estimates, communicating risks early, and delivering within agreed project budgets.
Explain technical decisions and data limitations clearly to non-technical client stakeholders.
Document architectures, environments, code, credentials, service accounts, and operational procedures to support effective client handover.
Mentor other engineers transitioning into data engineering and promote consistent engineering practices across projects.
Proven production experience with BigQuery and Dataform or dbt, including assertions, scheduled runs, data modeling, and production deployments.
Strong knowledge of the GA4 BigQuery export schema, including the ability to work with event parameters and transform raw exports into analytics-ready structures.
Hands-on experience developing and owning production-grade Python data pipelines, particularly API integrations involving pagination, rate limits, watermarks, idempotent backfills, and recovery processes.
Experience deploying containerized data services using Cloud Run or a comparable cloud-native platform.
Strong understanding of Google Cloud IAM, including service accounts, impersonation, permissions, and deployment or promotion across multiple GCP projects.
Experience integrating Looker or Looker Studio with BigQuery and tracing metrics from source events through data models to dashboard outputs.
Familiarity with cloud operations, including monitoring, secrets, storage, service-account management, cost controls, and production troubleshooting.
Strong analytical and problem-solving abilities, with the capacity to investigate data discrepancies and communicate the root cause clearly.
Experience working in client-facing environments with project managers, strategists, engineers, and business stakeholders.
Ability to estimate work accurately, identify scope changes early, manage priorities, and deliver within fixed project budgets.
Strong documentation and communication skills, with an ownership mindset and willingness to improve inherited systems and cloud environments.
Experience mentoring engineers or providing technical guidance to other team members is highly valuable.
Fully remote position open to candidates located anywhere in Latin America.
Contract-based engagement with opportunities to join a broader contractor network.
Compensation determined according to relevant experience, project background, geographic location, and business needs.
Opportunity to work on enterprise-grade data engineering projects for globally recognized brands.
Exposure to modern Google Cloud technologies, including BigQuery, Dataform, Cloud Run, IAM, and cloud monitoring.
Opportunity to collaborate with distributed US-based teams while working remotely.
Potential to be invited to future project engagements based on expertise and availability.
For contractors who demonstrate a strong fit and where business needs align, potential opportunities to explore full-time positions.
Opportunity to build a portfolio of production-grade data engineering work while expanding technical and client-facing skills.