Data Engineer — Global Data Pipelines & Governance

Amazon; Austin (TX); On-site; USD 132,000 - 179,000; Full time

Benefits offered by this job

Medical/Dental/Vision Coverage

PTO (Paid Time Off)

401(k) Plan

Parental Leave Options

Job summary

Amazon is seeking a Data Engineer to design and maintain large-scale data systems powering its global network and device infrastructure. You will develop robust pipelines, implement ETL, and collaborate with network engineering teams to meet analytical needs.

Role emphasizes data quality, governance, and security while participating in architecture discussions and evolving data engineering practices across the organization.

Qualifications

• 3+ years of data engineering experience with end-to-end ETL and data pipelines.
• Experience in data modeling, warehousing, and building scalable ETL solutions.
• Bachelor's degree in a relevant technical field or foreign equivalent.

Responsibilities

• Develop and maintain data pipelines ingesting data into our data lake, with ETL to cleanse and enrich data.
• Design scalable data solutions focusing on performance, quality, and reliability with cross-functional teams.
• Implement data quality controls, metadata management, and data lineage for governance and security.
• Establish data governance and security measures to protect sensitive information and ensure regulatory compliance.
• Monitor data infrastructure to identify and resolve issues, maintaining high availability.

Skills

3+ years data engineering

Data modeling & warehousing

Python development

Spark/Hadoop

ETL pipelines

Education

Bachelor's degree in computer science, engineering, analytics, or related field

Tools

AWS Cloud Services

Spark/Hadoop

Data lake / data warehouse tooling

Job description

Amazon is seeking a Data Engineer to design and maintain large-scale data systems powering its global network and device infrastructure. You will develop robust pipelines, implement ETL, and collaborate with network engineering teams to meet analytical needs.

Role emphasizes data quality, governance, and security while participating in architecture discussions and evolving data engineering practices across the organization.