Data Engineer

Luxoft; México; Remote; MXN 900,000 - 1,200,000; Full time

Job summary

Luxoft is seeking an autonomous data engineer to strengthen our data foundation for an energy platform using Ruby on Rails, Node.js/TypeScript and Python services on AWS. You will design robust pipelines and well-modeled schemas to turn raw data into analytics-ready insights.

You will collaborate with product teams to define criteria, onboard client data via Delta Sharing, and ensure reliable, automated pipelines with quality tests and schema-change handling.

Qualifications

• Own end-to-end production data pipelines as an autonomous IC.
• Experience designing and building production ingestion pipelines from APIs, files, and shared-data sources with a workflow orchestrator (Airflow, Dagster, 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 and scaling AI workflows across a team including CI/CD.
• Strong English communication skills.

Responsibilities

• Define and execute technical criteria with product teams.
• Architect and maintain data ingestion pipelines feeding both the app and analytics.
• Design well-modeled, layered tables with clean schemas for analytics-ready data.
• Onboard client data from sources (e.g. Databricks via Delta Sharing) and integrate with existing services/workflows.
• Make pipelines reliable and automated: data quality tests, monitoring, safe reprocessing and schema-change handling.
• Build a documented, well-tested data foundation for the app, analytics and future AI use cases.

Skills

End-to-end pipelines

SQL

Python

Node.js/TypeScript

Snowflake

AWS

AI-native development

English communication

Autonomous IC

Tools

Airflow

Dagster

Step Functions

Databricks

Delta Sharing

Terraform

Job description

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