Data Engineer - E-commerce Analytics (AWS)

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

Benefits offered by this job

Health insurance

401(k)

Paid time off

Parental leave

RSUs

Job summary

Amazon is seeking a Data Engineer for Whole Foods Market's E-commerce Analytics. You will architect robust data infrastructures and build advanced pipelines to unlock our expansive data landscape.

You will collaborate with BI Engineers and Data Scientists to deliver data-driven insights, optimize AWS-based data infrastructure, and drive architectural decisions for scalable data flows and storage.

Qualifications

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

Responsibilities

• Help build the infrastructure that powers data-driven decision making using best practices in software engineering and data management
• Manage and optimize usage of AWS resources to support data infrastructure
• Collaborate with BI Engineers to establish data reporting, integrity, testing, validation and documentation
• Collaborate with Data Scientists to implement analytics, prediction and ML algorithms on data
• Drive architecture and technology decisions to enable a world-class user experience
• Develop knowledge of Amazon data sources to determine resources for each use case
• Propose data flow and storage solutions and advocate for next-gen data architectures
• Create extensible, maintainable designs aligned with business objectives

Skills

Data modeling

ETL pipelines

AWS (Redshift/S3)

Distributed systems

Non-relational databases

Cloud resources management

Tools

AWS Glue

EMR

Kinesis

Lambda

Redshift

S3

Job description

Amazon is seeking a Data Engineer for Whole Foods Market's E-commerce Analytics. You will architect robust data infrastructures and build advanced pipelines to unlock our expansive data landscape.

You will collaborate with BI Engineers and Data Scientists to deliver data-driven insights, optimize AWS-based data infrastructure, and drive architectural decisions for scalable data flows and storage.