Software Development Engineer II, AWS Data Platform

About the role

Amazon Web Services is seeking an extraordinary Software Development Engineer with Data Engineering background to join the AWS Data Platform team. Our mission is to democratize access to trusted AWS business data — cataloging, governing, enriching, and brokering data through its lifecycle. We manage the core enterprise data infrastructure and curate foundational datasets from across AWS services, transforming raw data into actionable information that provides visibility into the state of the AWS business.

We support the full data lifecycle — ingestion, transformation, cataloging, governance, and consumption — at massive scale. We process trillions of events per month using stream processing (Kinesis), billions of line items via distributed compute (EMR and Spark), and store petabytes of data in open table formats (Apache Iceberg) on S3 Tables and Redshift. Our purpose-built tooling handles data ingestion, cataloging and discovery, governance, and self-service query access for internal service owners, analysts, scientists, and AI agents. We are increasingly leveraging generative AI and semantic layer technologies to make data more discoverable and queryable — enabling natural-language access to datasets and AI-powered recommendations that surface actionable intelligence for our data consumers.

Because we sit at the nexus of all AWS services, we work closely across teams to ensure a great consumer experience. You will have the ability to craft and build AWS's data platform and supporting systems for years to come — working at a scale where engineering decisions have meaningful, far-reaching impact.

Responsibilities

• Build and maintain data infrastructure using software engineering best practices, data management fundamentals, data storage principles, and operational excellence standards — creating datasets that analysts, scientists, and AI systems use to generate actionable insights.
• Develop automation and tooling that improves the reliability, scalability, and efficiency of data processing workflows across EMR, Spark, Redshift, and ingestion services.
• Design and implement data storage and compute solutions that balance cost, performance, and availability using distributed systems principles and open table formats (Hudi/Iceberg) to handle the ever-growing volume of AWS data.
• Own your services end to end: participate in on-call rotations, debug production issues, and continually reduce operational burden through directed engineering investments — fewer SEV2s, fewer manual interventions, more automation.
• Collaborate with business owners and internal stakeholders to understand data requirements and translate them into scalable, low-cost data flows from production systems into the data platform.
• Write design documents, participate in architecture reviews, and raise the engineering bar through design and code reviews.

A day in the life

Your primary focus is designing and building data infrastructure — ingestion pipelines, processing workflows, and the tooling that keeps them reliable at scale. You will write design documents, collaborate with teammates through code and architecture reviews, and invest in automation that reduces operational burden over time. You will work with massive datasets at the intersection of all AWS services, partnering with data engineers, scientists, and business intelligence teams who depend on the platform and the data it produces. On-call rotations are shared across the team; when issues arise, the expectation is to fix forward — resolving the immediate problem and improving the system so it does not recur.

About the team

You will work closely with teammates who jointly own these systems — collaborating through design reviews, shared on-call, and paired problem-solving. You will also partner with cross-functional stakeholders across AWS who produce or consume the data we manage. The team values engineering rigor, automation over heroics, and continuous improvement. We invest in reducing operational toil so engineers spend their time on meaningful, durable work.

Basic qualifications

• 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
• Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
• Experience operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets, or experience with data infrastructures: relational analytic DBMS, Elastic-Search, and Big Data EMR/EC2/Glue/Lambda
• Knowledge of data engineering pipelines, cloud solutions, ETL management, databases, visualizations and analytical platforms

Preferred qualifications

• 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