Data Engineer — AWS Big Data Pipelines & Analytics

Amazon; Ciudad de México; On-site; MXN 900,000 - 1,300,000; Full time

Job summary

Amazon Music is seeking a Data Engineer for the DISCO team to design and operate large-scale data pipelines and analytics platforms on AWS. You will model data, build warehousing solutions, and enable analytics and ML initiatives across the organization.

The role emphasizes scalable data engineering, collaboration with data scientists and product teams, and delivering self-service data capabilities for internal customers using Redshift, S3, EMR, and Spark-based tooling.

Qualifications

• 3+ years of data engineering experience.
• Experience with data modeling, warehousing and building ETL pipelines.

Responsibilities

• Design, develop and deliver scalable data analytics platform and data pipeline solutions to support Science and ML initiatives at scale.
• Help design, develop, and deliver components for the analytics platform at the broader org level and streamline workflows for DISCO.
• Collaborate with product managers, data scientists and engineers to build data solutions and ensure data quality and accessibility.
• Build data pipelines using SQL, PySpark and Airflow, ingesting data from diverse sources into structured formats.
• Create robust data models and data warehouses on AWS to enable BI and ML workloads.
• Document architecture, data models and APIs to facilitate knowledge sharing and maintainability.

Skills

Data modeling

Data warehousing

ETL pipelines

Tools

AWS Redshift

S3

AWS Glue

EMR

Kinesis Firehose

Lambda

IAM roles

Job description

Amazon Music is seeking a Data Engineer for the DISCO team to design and operate large-scale data pipelines and analytics platforms on AWS. You will model data, build warehousing solutions, and enable analytics and ML initiatives across the organization.

The role emphasizes scalable data engineering, collaboration with data scientists and product teams, and delivering self-service data capabilities for internal customers using Redshift, S3, EMR, and Spark-based tooling.