Project description

Our client is an energy solutions provider. Our AI-enabled platform supports operations, tracking and decision-making to optimize large-scale renewable and traditional energy portfolios. We're delivering a key client MVP in February 2027, and the project continues into 2028. Our stack: Ruby on Rails, Node.js/TypeScript and Python services, PostgreSQL, Snowflake and AWS. We're looking for an autonomous data engineer to strengthen our data foundation through good modeling, clean schemas, reliability and automation.

Responsibilities

- Work closely with product teams to help define and execute technical criteria
- Architect and maintain robust data ingestion pipelines that feed both our application and analytics
- Design well-modeled, layered tables with clean schemas that turn raw data into analytics-ready data
- Help onboard client data from a variety of sources, such as Databricks via Delta Sharing, and integrate it with our existing services and workflows
- Make pipelines reliable and automated: data quality tests, monitoring, safe reprocessing and schema change handling
- Build a documented, well-tested data foundation that serves the app,



analytics and future AI use cases

SKILLS

Must have

- Mid-to-senior data engineer who has owned the design of production data pipelines end to end, working as an autonomous IC
- Designing and building production ingestion pipelines from APIs, files and shared-data sources, using a workflow orchestrator (any tool, e.g. Airflow, Dagster or Step Functions)
- Production experience with a cloud data warehouse (Snowflake preferred)
- Strong SQL, plus production experience in Python (preferred) or Node.js/TypeScript
- AWS or another enterprise cloud platform
- AI-native development: daily use of agentic AI coding tools, and experience scaling shared AI workflows across a team and into the development lifecycle, including CI/CD
- Strong English and communication skills

Nice to have

- dbt
- Preparing data for AI/LLM use (e.g. semantic layers, metadata, retrieval-ready datasets)
- Infrastructure as code (e.g. Terraform)
- Exposure to energy, utilities or sustainability data

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📌 Data Engineer (México)
🏢 Luxoft
📍 México