Data Engineer Mexico

V4C.ai; México; Remote; MXN 420,000 - 660,000; Full time

Benefits offered by this job

Health insurance

Growth opportunities

Parental leave

Flexible schedule & remote work

Job summary

V4C.ai is seeking a hands-on Data Engineer to build scalable data pipelines within the Databricks ecosystem. You will bridge raw data and production-ready AI solutions, ensuring reliability, performance, and security.

The role focuses on scalable pipelines powering ML models and BI, with strong emphasis on data governance and cost-aware Databricks architecture. You will collaborate with data scientists and analysts to deploy models, optimize clusters, and contribute to best practices across the

Qualifications

• 1–4 years experience in data engineering or related field.
• Solid Databricks experience in platform engineering, data engineering and analytics.
• Proficiency in Python, Scala, or Java with Spark and Delta Lake.

Responsibilities

• Design, build, and maintain scalable data pipelines for AI/ML workflows.
• Create robust data lakes and warehousing solutions.
• Collaborate with data scientists to deploy ML models on Databricks.
• Optimize Databricks cluster performance for cost efficiency.
• Implement Databricks security features and ensure data governance.
• Support MLOps integration for production deployments.
• Contribute to data engineering standards and best practices.
• Stay updated on Databricks innovations and share knowledge.

Skills

Databricks platform engineering

Python/Scala/Java

Apache Spark

Delta Lake

ETL processes

Data governance

ML lifecycle support

Team collaboration

Communication

Tools

Databricks APIs

Apache Spark

Delta Lake

TensorFlow

PyTorch

Scikit-learn

Job description

Role 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.

Key Responsibilities

• AI/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.