Data Engineer: Build Scalable Pipelines & Real-Time Data

Spin; México; On-site; MXN 850,000 - 1,400,000; Full time

Job summary

Spin is seeking a Data Engineer to design, develop, and maintain robust data pipelines and architectures that support data-driven initiatives. You will contribute to all phases of the data engineering lifecycle, focusing on scalability, reliability, and performance.

You will mentor junior engineers, lead data processing initiatives, and collaborate with data scientists and analysts to deliver impactful data solutions.

Qualifications

• 3–4 years of experience as a Data Engineer.

Responsibilities

• Design and implement scalable data pipelines for ingesting, processing, and storing structured and semi-structured data from diverse sources.
• Collaborate with stakeholders to gather requirements, identify data sources, and define feasibility of solutions.
• Develop and optimize ETL processes, ensuring data accuracy, consistency, and integrity throughout its lifecycle.
• Create and maintain data models, adhering to best practices for normalization and performance optimization.
• Build and manage cloud-based data architectures, including data lakes, data warehouses, and real-time streaming solutions.
• Implement monitoring and alerting systems to ensure reliability and performance of data pipelines in production.
• Contribute to data governance by ensuring data quality, lineage, and security.
• Utilize Apache Spark, Kafka, and Flink for batch and real-time processing.
• Maintain and enhance CI/CD pipelines to automate deployment.
• Perform code reviews and enforce coding standards for quality.

Skills

Python

PySpark

SQL/NoSQL

Data modeling

ETL design

Cloud platforms

CI/CD for data

Data governance

Mentoring

Communication

Tools

Airflow

Kafka

Spark

Flink

Terraform

Job description

Spin is seeking a Data Engineer to design, develop, and maintain robust data pipelines and architectures that support data-driven initiatives. You will contribute to all phases of the data engineering lifecycle, focusing on scalability, reliability, and performance.

You will mentor junior engineers, lead data processing initiatives, and collaborate with data scientists and analysts to deliver impactful data solutions.