Data Engineer II: GenAI-Driven Real-Time Analytics

Amazon Inc.; Rockford (IL); Hybrid; USD 110,000 - 140,000; Full time

Job summary

Amazon DSP Analytics is seeking a Data Engineer to design and build data infrastructure supporting GenAI applications for the DSP program, turning ML research into scalable data products that inform decisions and delight customers.

You will lead complex data architectures, implement large-scale AWS pipelines, ensure governance and security, and collaborate with data scientists, economists, and analysts to deliver reliable insights across the organization.

Qualifications

• 3+ years of data engineering experience.
• Experience with data modeling, warehousing and ETL pipelines.
• Bachelor's degree in CS, Engineering, or related field.
• Proficiency in Python, Java, or similar languages.
• Experience with Redshift/NoSQL data stores.
• Experience with generative AI tools in projects.

Responsibilities

• Lead and design complex data architectures with scalability and security.
• Drive implementation of large-scale data pipelines and ETL processes using AWS technologies.
• Identify and resolve system-wide data challenges, including performance bottlenecks and architectural deficiencies.
• Establish data engineering best practices, including governance, security standards, and operational excellence.
• Ensure data solutions are auditable, accessible, and maintainable across the organization.
• Collaborate with cross-functional teams to deliver innovative data solutions.

Skills

Data modeling

ETL pipelines

Python

SQL

AWS

Generative AI tools

Big data

Education

Bachelor's degree in Computer Science or related field

Tools

Redshift

S3

Glue

EMR

Kinesis

Lambda

IAM

Job description

Amazon DSP Analytics is seeking a Data Engineer to design and build data infrastructure supporting GenAI applications for the DSP program, turning ML research into scalable data products that inform decisions and delight customers.

You will lead complex data architectures, implement large-scale AWS pipelines, ensure governance and security, and collaborate with data scientists, economists, and analysts to deliver reliable insights across the organization.