Senior Data Engineer (Databricks + Azure)

Nexaminds is actively seeking a Senior Data Engineer to join our dynamic engineering team. In this role, you will contribute to building and improving scalable data engineering solutions, data pipelines, and data models that enable teams across the organization to make better, data-driven decisions.

You’ll have the opportunity to work on ETL frameworks, data infrastructure, and self-service data tools, while collaborating with cross-functional teams to simplify access to reliable and actionable data. This role is ideal for engineers who enjoy solving complex data problems, building scalable solutions, and working with modern data engineering technologies.

Location: Mexico (Remote)

This position is open exclusively to candidates who are currently residing in Mexico. Before applying, we encourage you to review our open positions and make sure the location requirements match your current location. Candidates who are not currently based in Mexico will not be contacted for this opportunity.

Requirements:

5+ years of professional experience in Data Engineering or a closely related field.

Strong experience with data modeling and data pipeline/job development.

Production-level programming experience with Python and SQL.

Hands-on experience with technologies such as Apache Spark, dbt, Fivetran, and Airflow.

Experience working with cloud-based data platforms and S3-based data warehouses or data lakes.

Experience with ETL/ELT processes and building scalable data solutions.

Experience using AI-assisted coding tools, such as Claude Code, Codex, or similar technologies.

Strong problem-solving and analytical skills, with the ability to simplify complex data problems.

Experience collaborating with cross-functional teams and business stakeholders to gather requirements and translate them into technical solutions.

Strong communication skills and a proactive approach to collaboration.

Bachelor’s degree in Computer Science, Engineering, or a related field.

Ability to work independently, take ownership, and contribute ideas that improve data infrastructure and engineering practices.

Responsibilities:

Design, develop, and maintain scalable data pipelines and ETL/ELT processes.

Contribute to the evolution of data engineering frameworks and infrastructure.

Build and maintain reliable data models and metric definitions that support analytics, data science, and business decision-making.

Develop production-quality solutions using Python and SQL.

Work with technologies such as Spark, dbt, Fivetran, Airflow, and cloud-based data storage.

Collaborate with data scientists, engineers, analysts, and business stakeholders to understand data needs and deliver effective solutions.

Identify opportunities to improve data quality, reliability, scalability, and accessibility.

Contribute ideas and technical recommendations that help shape the evolution of the data platform and engineering ecosystem.

Use AI-assisted development tools to improve productivity, understand existing systems, and accelerate software development.

Help create self-service data tools and solutions that make it easier for teams to access and work with data.

Troubleshoot data pipeline and infrastructure issues and implement improvements to ensure reliable data delivery.

Continuously learn and adopt modern practices across data engineering, cloud technologies, automation, and AI-assisted development.