Global Data Engineer — Leo Operations & GenAI Insights

Amazon; Arlington (VA); On-site; USD 132,000 - 179,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

RSUs

Job summary

Amazon Leo is seeking a Data Engineer to build and own the pipelines powering Leo's data platform. You will ingest data from dozens of sources, enforce quality standards, and drive operational efficiency to scale a rapidly growing satellite business.

You'll partner with business operations, data science, and engineering teams to deliver curated datasets, standardized metrics, and centralized tooling while upholding security and governance across multi-tenant cloud environments.

Qualifications

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

Responsibilities

• Data Pipeline Development: Build, maintain, and scale data pipelines that ingest data from first-party (Leo catalog, order management), second-party (AWS services), and third-party sources into a unified data lake.
• Data Quality and Governance: Define and implement data quality checks, validation frameworks, and monitoring alerts.
• Operational Efficiency: Identify and eliminate bottlenecks in data workflows, automate manual processes, reduce pipeline latency and improve cost efficiency.
• Cross-Team Partnership: Collaborate with business operations, data science, and engineering teams to deliver curated, reliable datasets.
• Infrastructure Standards: Apply security best practices and compliance requirements to data systems across multi-tenant platforms.

Skills

Data engineering

Data modeling

ETL pipelines

SQL

AWS data services

Tools

Redshift

S3

AWS Glue

EMR

Kinesis

Firehose

Lambda

IAM

DynamoDB

Job description

Amazon Leo is seeking a Data Engineer to build and own the pipelines powering Leo's data platform. You will ingest data from dozens of sources, enforce quality standards, and drive operational efficiency to scale a rapidly growing satellite business.

You'll partner with business operations, data science, and engineering teams to deliver curated datasets, standardized metrics, and centralized tooling while upholding security and governance across multi-tenant cloud environments.