Remote Data Engineer (Mexico) - Databricks & AI/ML Pipelines

NEPSE Trading; México; A distancia; MXN 420.000 - 780.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Health insurance

Flexible schedule and remote working

Employee rewards program and paid Par‑

Descripción de la vacante

NEPSE Trading in Mexico is seeking a hands-on Data Engineer to build robust data pipelines and analytics foundations for AI-driven workloads. You will bridge raw data to production-ready solutions, focusing on Databricks-based pipelines, data lakes, and data warehousing, while collaborating with data scientists and analysts.

The role emphasizes performance, cost-efficiency, data governance, and security within the Databricks ecosystem, with exposure to ML lifecycles and MLOps practices.

Formación

• 1-4 years of data engineering or related experience.
• Solid Databricks expertise in platform engineering, data engineering and analytics.
• Proficient in Python, Scala or Java; strong knowledge of Spark and Delta Lake.
• Experience with data warehousing, ETL processes, and data governance frameworks.
• Practical exposure to AI/ML lifecycles and MLOps concepts.
• Ability to work independently, own projects, and collaborate with a team.

Responsabilidades

• Architect, build and maintain scalable data pipelines for AI/ML workflows.
• Design data lakes and warehousing solutions and deploy ML models with analysts.
• Improve Databricks cluster performance and enforce security and governance.
• Collaborate with data scientists to productionize models and analytics.
• Stay updated on Databricks innovations and share best practices across the team.

Conocimientos

Data engineering

Problem solving

Ownership

Independent work

Cross-functional collaboration

Herramientas

Databricks

Python

Scala

Java

Apache Spark

Delta Lake

AWS

Azure

GCP

TensorFlow

PyTorch

Scikit-learn

Descripción del empleo

NEPSE Trading in Mexico is seeking a hands-on Data Engineer to build robust data pipelines and analytics foundations for AI-driven workloads. You will bridge raw data to production-ready solutions, focusing on Databricks-based pipelines, data lakes, and data warehousing, while collaborating with data scientists and analysts.

The role emphasizes performance, cost-efficiency, data governance, and security within the Databricks ecosystem, with exposure to ML lifecycles and MLOps practices.