Software Development Engineer, Advanced Analytics - Lld

Amazon Amazon · Big Tech · Seattle, WA · Software Development

This role focuses on building and maintaining foundational infrastructure for capturing, processing, storing, and analyzing petabyte-scale log-level advertising data. The goal is to provide transparency and enable data-driven decisions for advertisers within Amazon Ads. It involves creating scalable data pipelines, ETL workflows, APIs, and ensuring data quality and performance.

What you'd actually do

  1. Implement and maintain scalable data pipelines to ingest, process, and store petabyte-scale log-level advertising data from multiple sources (ad servers, exchanges, SSPs)
  2. Build ETL workflows that transform raw event-level data into queryable, analysis-ready formats following established architectural patterns and best practices
  3. Create and maintain APIs that enable analytics teams and data scientists to query log-level data efficiently
  4. Implement data quality checks, validation logic, and monitoring for data pipelines and services
  5. Optimize performance and cost efficiency of data processing jobs and queries

Skills

Required

  • 3+ years of non-internship professional software development experience
  • 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
  • 1+ years of software development engineer or related occupational experience
  • 1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
  • 1+ years of Object Oriented Design experience
  • Experience programming with at least one software programming language

Nice to have

  • 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
  • Bachelor's degree in computer science or equivalent
  • Experience using managed ML/AI solutions
  • Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience leading the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems