Data Engineer

Own the design and operation of production data pipelines that support an application, analytics, and future AI use cases. The work spans onboarding data from varied sources, shaping it into reliable, well-modeled datasets, and coordinating with product teams on technical needs.

• Define and maintain ingestion pipelines that serve application and analytics workflows.
• Transform raw data into layered, analytics-ready tables with clear schemas.
• Onboard data from sources such as APIs, files, and shared data platforms, and connect it to existing services.
• Improve pipeline reliability through data quality checks, monitoring, safe reprocessing, and handling schema changes.
• Document and test the data foundation so it can support current products and future AI applications.
• Work with product teams to clarify and carry out technical criteria.

• Mid-to-senior data engineering experience, including autonomous ownership of production pipelines from design through operation.
• Experience building ingestion from APIs, files, or shared data sources with a workflow orchestrator such as Airflow, Dagster, or Step Functions.
• Production experience with a cloud data warehouse;
  Snowflake is preferred.
• Strong SQL and production programming experience in Python or Node.js/Type Script;
  Python is preferred.
• Experience with AWS or another enterprise cloud platform.
• Daily use of agentic AI coding tools, plus experience scaling shared AI workflows across a team and into development and CI/CD.
• Strong English and communication skills.

• Experience with dbt, infrastructure as code such as Terraform, or preparing data for AI/LLM use through semantic layers, metadata, or retrieval-ready datasets.
• Familiarity with energy, utilities, or sustainability data.

• Remote, full-time contract for candidates based in Latin America, with a 40-hour workweek.
• Listed monthly compensation is USD $3,000–$4,000, depending on experience and skills.