Data Engineer — Scalable Pipelines & Cloud Data Architect

Spin - Job Board External; México; On-site; MXN 600,000 - 1,200,000; Full time

Job summary

Spin is seeking a Data Engineer to design, develop, and maintain robust data pipelines and architectures that power data-driven initiatives in Mexico. You will contribute across the data engineering lifecycle, ensuring scalability, reliability, and performance while mentoring junior engineers and improving practices.

You will work with stakeholders to gather requirements, build scalable ETL processes, and implement cloud-based data architectures including data lakes, warehouses, and streaming

Qualifications

• Minimum 3 - 4 years of experience as a Data Engineer.
• Strong Python, including optimized code and advanced features.
• Extensive experience with Pandas, NumPy, PySpark, and Dask.
• Solid SQL and NoSQL understanding with optimization skills.
• Experience with data modeling, ETL, and data warehousing techniques.

Responsibilities

• Design scalable data pipelines to ingest, process, and store data from diverse sources.
• Collaborate with stakeholders to gather requirements and define feasible solutions.
• Develop and optimize ETL processes ensuring data accuracy and integrity.
• Create and maintain data models for normalized, high-performance schemas.
• Build cloud-based data architectures (data lakes/warehouses) and real-time streaming solutions.
• Implement monitoring and alerting for production data pipelines.

Skills

Python

SQL

Data modeling

ETL design

Cloud platforms

CI/CD

Team leadership

Data governance

Tools

Apache Spark

Apache Kafka

Apache Flink

Pandas

NumPy

PySpark

Dask

Job description

Spin is seeking a Data Engineer to design, develop, and maintain robust data pipelines and architectures that power data-driven initiatives in Mexico. You will contribute across the data engineering lifecycle, ensuring scalability, reliability, and performance while mentoring junior engineers and improving practices.

You will work with stakeholders to gather requirements, build scalable ETL processes, and implement cloud-based data architectures including data lakes, warehouses, and streaming