Software Engineer II, Selection Monitoring

Amazons Selection Monitoring team is responsible for building and operating large-scale data processing systems that power catalog intelligence across Amazon. Our systems process billions of products from multiple data sourcesboth real-time crawler feeds and pre-computed datasetsto create comprehensive high-quality product information that drives business decisions across Amazon. We apply parallel processing machine learning and deep learning algorithms to evaluate and enrich product data at global scale. The datasets and insights produced by our team enable teams across Amazon to make data-backed decisions that improve customer experience.

Our work involves building state-of-the-art data processing pipelines and Information Retrieval (IR) systems that handle diverse data streams and automatically create structured entities from unstructured/semi-structured data. We constantly stretch the boundaries of large-scale distributed systems streaming and batch processing technologies Elastic Computing Big Data Artificial Intelligence and service-oriented architectures to tackle challenges at Amazons global scale.

Come join us in our journey to build the data infrastructure that powers Amazons catalog intelligence!
We are looking for SDEs with strong technical knowledge established background in engineering large-scale software systems and passion for solving challenging problems. The role demands a high-performing and flexible candidate who can take responsibility for system success and drive solutions from design to coding testing and deployment to achieve results in a fast-paced environment.

The SDE on this team will play a key role in solving complex problems and building innovative solutions that process and integrate data from multiple sources at scale. Candidates need to have a high sense of ownership ability to develop end-to-end solutions and drive measurable impact. This role will expose you to solving problems of scale in the areas of data engineering real-time and batch processing observability artificial intelligence big data and distributed systems of cloud-scale.

Key job responsibilities
* Design and build scalable data processing systems that handle both real-time crawler data and precomputed datasets from multiple sources ensuring seamless integration and orchestration across diverse data streams.
* Develop robust pipelines and frameworks for ingesting processing transforming and enriching product data at scale using AWS technologies including streaming and batch processing platforms.
* Work with Senior Engineers to drive the technical and architectural vision for systems responsible for generating structured domain entities from structured/semi-structured data across multiple crawler sources.
* Build comprehensive observability and metrics systems to proactively identify bottlenecks monitor system health and provide accurate actionable reporting that enables data-backed decision making.
* Solve complex problems in data integration entity resolution deduplication and classification across heterogeneous data sources with varying quality and formats.
* Design and implement efficient storage solutions for managing large-scale datasets ensuring optimal performance for both write-heavy ingestion and read-heavy analytical workloads.
* Lead and mentor engineers and drive best practices around design coding testability operational excellence and security.
* Own end-to-end delivery of critical systems from requirements gathering through design implementation testing deployment and ongoing operational support.

- 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
- Experience programming with at least one software programming language

- 3 years of full software development life cycle including coding standards code reviews source control management build processes testing and operations experience
- Bachelors degree in computer science or equivalent

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