Data Engineer

Location: Montevideo, Uruguay; Remote.AR, Remote.LATAM-country; Remote.CO, Colombia; Rosario, Remote.LATAM-country We are looking for a hands-on Data Engineer (Middle/Senior) to design, build, and maintain reliable, scalable pipelines that keep information timely, clean, secure, and well-structured for downstream consumers. This is an execution-focused role combining pipeline development, query and cost optimization, data governance, and production monitoring. The engineer will work closely with business, analytics, and data science teams to make data available and trustworthy across the organization. The Data & AI chapter of the Passenger Journey builds and operates the data foundation that turns raw data into trusted, strategic assets for analytics, product, and business teams. The team works on Google Cloud Platform (GCP) under Agile and DataOps practices, with a tech stack centered on BigQuery and SQL, Dataform (including assertions) for transformation, and Python (Pandas) for processing and automation. Pipelines run on Cloud Run, Cloud Run Jobs, Dataflow, Pub/Sub, Cloud Storage, Cloud SQL, and Cloud Scheduler, with Git/GitLab, CI/CD, and Terraform for delivery, and IAM and policy tags for access control. Data is organized under a medallion architecture (Bronze, Silver, Gold), with cost and performance optimization (FinOps) treated as a first-class concern. Design, implement, and maintain reliable, scalable ingestion and transformation pipelines that ensure data quality, integrity, and availability for business and analytics. Optimize processing and storage by reducing execution time and costs (FinOps) across pipelines and queries to maximize the value of data. Apply data governance standards, including quality, naming conventions, cataloging, and a medallion architecture (Bronze, Silver, Gold), to ensure consistency and traceability. Protect sensitive data through role-based access control (IAM) and policy tags, ensuring compliance with applicable regulations and standards (GDPR, LGPD, ISO, and local regulations). Monitor pipelines, respond to incidents, and adapt them to new business requirements while meeting availability and performance SLAs. Enable controlled, cross-functional access to data for broad consumption. 3+ years of proven experience as a Data Engineer or in a similar role. Strong SQL skills and hands-on experience with BigQuery, including query optimization, partitioning, and clustering. Experience building and maintaining ELT/ETL pipelines and data models with Dataform (or a similar framework, such as dbt, with skills readily transferable to Dataform), including assertions. Experience with Python for data processing and automation (Pandas). Solid GCP experience across Cloud Run / Cloud Run Jobs, Dataflow, Pub/Sub, Cloud Storage, Cloud SQL, and Cloud Scheduler. Knowledge of medallion architecture (Bronze/Silver/Gold) and sound data-modeling fundamentals. Knowledge of data governance and compliance, including IAM, policy tags, and regulatory awareness (GDPR, LGPD, ISO). Experience with Git/GitLab, CI/CD, and Terraform for delivery and infrastructure as code. Experience with pipeline monitoring and observability (Cloud Monitoring; Grafana is desirable). Exposure to GenAI-assisted development and tooling, including prompt engineering, OpenAI API/SDK, Vertex AI, and MCP (desirable). Experience with API development using FastAPI / REST services (desirable). Experience with BI and data visualization tools such as Looker Studio or Google Data Studio (desirable). Ability to communicate effectively with technical and non-technical stakeholders, and experience working in Agile environments. Fluency in Spanish (the team's working language) and intermediate English proficiency for documentation and international collaboration.