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Software Development Engineer, Measurement, AdTech, and Data Science

Amazon.com Inc
  • Seattle, WA
    2 days ago

    Job Description

    Build the attribution systems that connect ad interactions with real customer outcomes - processing more than 50 billion events per day to produce the measurements that power experiences across all of Amazon Ads and help millions of advertisers understand the true value of their campaigns. Attribution is at the heart of advertising measurement, and your work will directly influence how advertising budgets are optimized at global scale.

    Building accurate attribution at Amazon scale is one of the most challenging engineering problems in advertising. Our systems connect signals across advertising products, customer journeys, devices, channels, and conversion sources - solving problems involving massive-scale data processing, distributed systems, identity and signal fragmentation, evolving privacy requirements, and complex attribution methodologies. The measurements we produce provide foundational signals for reporting, campaign optimization, machine learning models, analytics applications, and emerging AI-powered advertising experiences.

    What makes this role stand out: • 50B+ events per day - petabyte-scale data pipelines using Spark, EMR, Apache Iceberg, DynamoDB, and other AWS services • Engineering meets science - you"ll work directly with applied scientists and economists to translate new measurement methodologies into scalable production systems • End-to-end ownership - from understanding an ambiguous measurement problem through design, implementation, launch, and operations • Direct business impact - the data you produce helps millions of advertisers make better investment decisions and powers optimization across Amazon"s full advertising suite • Build new, not just maintain - opportunities to design new architectures that improve scalability, data freshness, correctness, reliability, and cost efficiency

    Key job responsibilities

    • Design, develop, and deliver software that produces measurement data consumed across Amazon"s full advertising suite - spanning display, search, native, and video
    • Build and maintain petabyte-scale data pipelines and services using technologies like Spark, EMR, Apache Iceberg, and Java, inventing new big data paradigms to keep pace with rapid growth
    • Navigate complex attribution challenges with creative problem-solving - adapting to evolving technical requirements, privacy landscapes, and shifting priorities
    • Collaborate with applied scientists, economists, product managers, and engineers across teams to translate measurement methodologies into scalable production systems
    • Apply a variety of architectural approaches and design patterns to deliver maintainable, scalable, and well-tested solutions in a high-throughput environment
    • Drive engineering best practices including code reviews, testing strategies, operational excellence (alarms, runbooks, monitoring), and continuous improvement
    • Contribute to technical design discussions, identify trade-offs, and propose solutions that balance performance, cost, and long-term maintainability

    About the team

    The Measurement, AdTech, and Data Science (MADS) team defines and produces the metrics advertisers rely on to analyze the performance of their ad investments. Our charter is focused on the systems responsible for computing and distributing estimated conversions at massive scale. We work across a rich technology stack - combining AWS services like EMR, Kinesis, and AWS Batch with open-source technologies such as Spark and Presto - to deliver fast, reliable measurement at the speed advertisers demand.

    Numbers & Facts

    LocationSeattle, WA
    IndustryRetail
    Company Size10,000 employees or more
    Year Founded1994
    Websitehttp://Amazon.com/militaryroles

    About Company

    At Amazon, we don’t wait for the next big idea to present itself. We envision the shape of impossible things and then we boldly make them reality. So far, this mindset has helped us achieve some incredible things. Let’s build new systems, challenge the status quo, and design the world we want to live in. We believe the work you do here will be the best work of your life.

    Wherever you are in your career exploration, Amazon likely has an opportunity for you. Our research scientists and engineers shape the future of natural language understanding with Alexa. Fulfillment center associates around the globe send customer orders from our warehouses to doorsteps. Product managers set feature requirements, strategy, and marketing messages for brand new customer experiences. And as we grow, we’ll add jobs that haven’t been invented yet.

    It’s Always Day 1
    At Amazon, it’s always “Day 1.” Now, what does this mean and why does it matter? It means that our approach remains the same as it was on Amazon’s very first day – to make smart, fast decisions, stay nimble, invent, and stay focused on delighting our customers. In our 2016 shareholder letter, Amazon CEO Jeff Bezos shared his thoughts on how to keep up a Day 1 company mindset. “Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight,” he wrote. “A customer-obsessed culture best creates the conditions where all of that can happen.” You can read the full letter here

    Our Leadership Principles
    Our Leadership Principles help us keep a Day 1 mentality. They aren’t just a pretty inspirational wall hanging. Amazonians use them, every day, whether they’re discussing ideas for new projects, deciding on the best solution for a customer’s problem, or interviewing candidates. To read through our Leadership Principles from Customer Obsession to Bias for Action, visit https://www.amazon.jobs/principles

    Skills

    • Adtechunmatched
    • Advertisingunmatched
    • Amazon Web Services (AWS)unmatched
    • Apacheunmatched
    • Architectural Designunmatched
    • Architectural Servicesunmatched
    • Artificial Intelligence (AI)unmatched
    • Best Practicesunmatched
    • Big Dataunmatched
    • Budgetingunmatched
    • Campaignsunmatched
    • Code Reviewsunmatched
    • Continuous Improvementunmatched
    • Customer Relationsunmatched
    • Data Managementunmatched
    • Data Processingunmatched
    • Data Scienceunmatched
    • Design Patterns Programming Methodologiesunmatched
    • Distributed Computingunmatched
    • Electronic Medical Recordsunmatched
    • High Throughputunmatched
    • Javaunmatched
    • Machine Learningunmatched
    • Metricsunmatched
    • Open Sourceunmatched
    • Operational Strategyunmatched
    • Performance Analysisunmatched
    • Problem Solving Skillsunmatched
    • Product Engineeringunmatched
    • Production Systemsunmatched
    • Software Developmentunmatched
    • Software Engineeringunmatched
    • Systems Scalabilityunmatched
    • Technical/Engineering Designunmatched
    • Test Strategyunmatched

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