Senior/Lead Data Engineer – AI-Native Aftermarket Platform

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior/Lead Data Engineer – AI-Native Aftermarket Platform based in Mexico.

This is a senior technical leadership role focused on building and scaling the data foundations of an AI-native platform transforming the global equipment aftermarket.
You will design and own end-to-end data pipelines that power machine learning models, analytics, and operational decision-making across large-scale datasets.
The role combines deep hands-on engineering with architectural leadership, ensuring high standards for performance, reliability, and data quality.
You will work with a modern data stack, leveraging tools such as Python, SQL, dbt, Spark, and Databricks in a cloud-native environment.
Beyond execution, you will help define engineering standards, data modeling practices, and governance frameworks across the data ecosystem.
The position also includes mentoring other engineers and influencing technical direction across multiple repositories and teams.
This is a highly impactful role in a fast-moving, AI-driven environment where data is central to product innovation.

Accountabilities:

• Design, build, and maintain scalable, idempotent end-to-end data pipelines using modern data stack principles to support analytics and AI workloads.
• Develop robust data models (star and snowflake schemas) and write high-quality, grain-aware SQL to build scalable and reliable data marts.
• Build production-grade Python systems with strong engineering discipline, including testing, type safety, and modular design.
• Develop and manage dbt models across layered architectures (staging, intermediate, marts), ensuring strong testing and documentation standards.
• Implement and enforce data quality frameworks, including validation checks, schema enforcement, and anomaly detection across pipelines.
• Develop and deploy Databricks-based workloads (including Asset Bundles) and operate within secure, governed cloud environments.
• Provide technical leadership by defining architecture standards, reviewing code, mentoring engineers, and guiding cross-team engineering decisions.
• Ensure reliability and observability of data systems, while proactively identifying and resolving performance or pipeline issues.

Requirements:

• Strong expertise in SQL and dimensional data modeling, including medallion architecture, SCD patterns, and dataset grain management.
• Extensive experience building production-grade data pipelines using Python, with strong testing practices (pytest), typing, and code quality tools.
• Deep hands-on experience with Spark/PySpark and performance troubleshooting using tools such as Spark UI.
• Strong experience with dbt, including model development, testing frameworks, and data documentation practices.
• Solid knowledge of Databricks, Delta Lake (MERGE, OPTIMIZE, Z-ORDER, time travel), and modern lakehouse architectures.
• Experience working with cloud data platforms and tools such as GitHub, CI/CD workflows, and secret management systems.
• Proven ability to operate in complex, distributed systems and make architectural trade-offs between cost, scalability, and performance.
• Strong communication skills with the ability to document technical decisions and translate engineering work into business impact.
• Leadership experience mentoring engineers, setting technical direction, and contributing to engineering best practices.
• Familiarity with Azure ecosystem tools (ADF, ADLS) and modern observability or AI-assisted engineering tools is a plus.

Benefits:

• Fully remote role with complete flexibility in working location and schedule.
• Highly competitive USD-based compensation aligned with senior/lead-level expertise.
• Paid time off to support rest, recovery, and work-life balance.
• Opportunity to work on cutting-edge AI-native platforms with high-impact, large-scale data systems.
• Exposure to leading U.S.-based companies and complex global engineering environments.
• Collaborative, high-seniority engineering culture focused on autonomy, ownership, and technical excellence.
• Strong professional growth opportunities within advanced data and AI engineering domains.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether?    Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.     #LI-CL1