Data Engineer Mexico

NEPSE Trading; México; Remote; MXN 420,000 - 780,000; Full time

Benefits offered by this job

Health insurance

Flexible schedule and remote working

Employee rewards program and paid Par‑

Job summary

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.

Qualifications

• 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.

Responsibilities

• 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.

Skills

Data engineering

Problem solving

Ownership

Independent work

Cross-functional collaboration

Tools

Databricks

Python

Scala

Java

Apache Spark

Delta Lake

AWS

Azure

GCP

TensorFlow

PyTorch

Scikit-learn

Job description

## About the roleRole Overview We are seeking a hands-on Data Engineer to join our team, focusing on building the robust foundations required for modern analytics and AI. In this role, you will bridge the gap between raw data and production-ready AI solutions, architecting scalable pipelines that power machine learning models and business intelligence. You will be responsible for the engineering discipline that ensures data reliability, performance, and security within the Databricks ecosystem.### ResponsibilitiesAI/ML Pipeline Support: Architect, build, and maintain scalable data pipelines specifically designed to support AI/ML workflows, including feature engineering and model training datasets. Design and implement robust data pipelines, data lakes, and data warehousing solutions. Partner with data scientists and analysts to deploy machine learning models and analytics solutions on Databricks.Enhance Databricks cluster performance, ensuring cost-effectiveness and efficient resource utilization. Implement Databricks security features, enforce data governance, and maintain compliance with industry standards. MLOps Integration: Collaborate with Data Scientists to deploy and operationalize machine learning models into production environments securely.Contribute to the technical direction and best practices of data engineering initiatives. Stay current with Databricks innovations, best practices, and industry trends, and share knowledge across the team.### Required Skills& Qualifications Total Experience: 1-4 years in data engineering or related fields. Databricks Expertise: Solid experience with Databricks, focusing on platform engineering, data engineering, and analytics. Technical Skills: Proficiency in Python, Scala, or Java; strong experience with Databricks APIs, Apache Spark, and Delta Lake.Data Engineering Background: Solid experience in data warehousing, ETL processes, and data governance frameworks. AI/ML Exposure: Practical experience supporting AI/ML lifecycles, including familiarity with ML frameworks (e.g., TensorFlow, PyTorch) or MLOps practices. Collaboration & Ownership: Proven ability to work independently, take ownership of projects, and collaborate effectively with a team.Communication: Excellent interpersonal and communication skills for effective collaboration with stakeholders. Good to Have Cloud Experience: Hands-on experience with AWS, Azure, or GCP. Certifications: Databricks Certified Professional or equivalent.Machine Learning: Familiarity with ML concepts and popular libraries (e.g., TensorFlow, PyTorch, Scikit-learn). What we are offering Best-in-industry learning and growth opportunities. Comprehensive Health and wellness### benefits*, including medical insurance for self and family* Employee rewards program and paid parental leave* Flexible schedule and remote working benefits* Originally posted on Himalayas## Required skillsData EngineerBig Data EngineerAnalytics EngineerData Platform EngineerMLOps EngineerpythonawsLocation: Mexico