Drimva

Amazon

Sr. Software Development Engineer, Amazon Athena - Engine

$185K–$250K

New York, NY · Senior · Full-time · Posted Jun 30, 2026

Experience
5+ years in each of the stated professional software development, programming, and design or architecture areas; preferred qualifications also include 5+ years of full software development life cycle experience and 4+ years of highly scalable systems experience.
Education
Bachelor's degree in computer science or equivalent.

About the job

Develop distributed Athena capabilities that help customers understand query cost, performance, and failures, and extend Athena through features such as Federated Query and User Defined Functions.

What you'll do

  • Build solutions that provide customers with insights into Athena usage and help them extend Athena, scale, and automate operations.
  • Partner across analytics, control plane, and engine teams to develop and execute the roadmap.
  • Coach and mentor engineers and identify customer experience gaps to address.

What they ask for

  • 5+ years of non-internship professional software development experience.
  • 5+ years of programming experience with at least one software programming language.
  • 5+ years of leading design or architecture of new and existing systems, including design patterns, reliability, and scaling.
  • Experience as a mentor, tech lead, or leader of an engineering team.

Nice to have

  • 5+ years of full software development life cycle experience, including coding standards, code reviews, source control management, build processes, testing, and operations.
  • Bachelor's degree in computer science or equivalent; 4+ years of highly scalable systems experience; or experience with machine learning systems or highly available distributed data systems.

Pay

184,900.00 - 250,200.00 USD annually (base salary)

Tools

Amazon Athena, Amazon S3, SQL, Federated Query, User Defined Functions, AI/ML

AI focus

Use AI/ML to generate customer insights and optimizations; preferred experience includes training and deploying machine learning systems for large-scale optimizations.

Model-written summary · the original posting is authoritative

First seen Oct 15, 2026 · Last verified Nov 4, 2026 (UTC)