Google Cloud Lead Data Engineer

Accountabilities

- 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.

Requirements

- 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.

Benefits

- 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.

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