Data Engineer

Role overview Build and operate data pipelines that turn raw information into reliable, well-governed datasets for analytics, product, and business use. You’ll work across ingestion, transformation, performance, and production support, partnering with business, analytics, and data science teams in an Agile, DataOps environment on Google Cloud.

Responsibilities - Develop and maintain scalable ingestion and transformation pipelines, with checks that protect data quality and availability. - Improve query and pipeline performance, storage efficiency, and cloud costs. - Organize datasets using a Bronze, Silver, and Gold medallion model, with consistent naming, cataloging, and traceability. - Apply access controls and data protection measures, including IAM roles and policy tags. - Monitor production pipelines, investigate incidents, and adapt services to changing requirements and availability targets. - Make data accessible to teams across the organization while maintaining appropriate controls.

Requirements - At least 3 years of experience in data engineering or a similar role. - Strong SQL and hands-on BigQuery skills, including query tuning, partitioning, and clustering. - Experience building ELT or ETL pipelines and data models with Dataform or a transferable framework such as dbt, including assertions. - Python skills for data processing and automation, including Pandas. - Practical experience across GCP services such as Cloud Run, Dataflow, Pub/Sub, Cloud Storage, Cloud SQL, and Cloud Scheduler. - Knowledge of data modeling, governance, and compliance; experience with Git, CI/CD, and Terraform; and the ability to communicate with technical and non-technical stakeholders. - Fluent Spanish and intermediate English for documentation and collaboration.

Nice to have - Pipeline observability with Cloud Monitoring or Grafana. - GenAI-assisted development, prompt engineering, OpenAI or Vertex AI tools, or MCP. - FastAPI or REST development, and BI tools such as Looker Studio.