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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer, Infrastructure Reliability - **Company:** Amazon.com, Inc. - **Location:** Austin, TX, United States - **Experience:** Expert - **Salary:** $154,600.0 - $209,100.0 - **Contract:** Temporary contract - **Skills:** Java (Programming Language), Artificial Intelligence, Data Analysis, Batch Processing, Big Data, Software Quality, Information Engineering, Extract Transform Load (ETL), Data Retention, Amazon DynamoDB, Apache Hadoop, Apache Hive, Python (Programming Language), Machine Learning, Node.Js, Operational Databases, Standard Sql, DataOps, Scala (Programming Language), Scripting, Feature Engineering, Data Ingestion, Apache Spark, Electronic Medical Records, Data Lakes, Information Technology, Cloudwatch, Programming Languages - **Published:** August 2, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=798060ec2cfd58e6 ## About the Role * 5+ years of data engineering experience * Experience with data modeling, warehousing and building ETL pipelines * Experience with SQL * Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS * Experience mentoring team members on best practices * Bachelor's degree in computer science, engineering, analytics, mathematics, statistics, IT or equivalent, * Experience with big data technologies such as: Hadoop, Hive, Spark, EMR * Experience operating large data warehouses * Master's degree in computer science, engineering, analytics, mathematics, statistics, IT or equivalent ## Description Help build the data foundation that keeps Amazon's fulfillment network running 24/7. Infrastructure Reliability is building an AI-powered platform that detects, diagnoses, and resolves incidents across thousands of sites globally, and none of it works without clean, reliable, well-modeled data. You will own and evolve production data pipelines that power real-time incident detection and correlation, build the data lake and unified data models that unblock ML and autonomous resolution initiatives, and rescue historical telemetry data before it expires and becomes permanently unrecoverable. This is a high-ownership role with direct, visible impact on Amazon's global fulfillment operations, working closely with applied scientists who train models on the data you build., You will design, build, and operate ETL pipelines that ingest, transform, and correlate incident and telemetry data from sources including DynamoDB, OpenSearch, CloudWatch, and internal incident management systems, publishing curated datasets to our data lake and Andes for consumption by dashboards, applications, and ML models. You will take ownership of production pipelines, including scheduled batch processing jobs and Glue-based ETL workflows, ensuring reliability, monitoring, and timely resolution of pipeline issues. You will build data retention and ingestion pipelines to preserve time-bound telemetry signals before source-system expiration windows, and you will design standardized ingestion frameworks that normalize data from many disparate sources into a common, science-consumable format. You will contribute to the design of a unified data lake architecture, including schema design, partitioning strategy, and access patterns, replacing fragmented, duplicated pipelines with a single source of truth. You will partner closely with applied scientists and engineers to grasp data requirements for ML model training and feature engineering, and you will mentor other engineers on data engineering best practices, code quality, and pipeline design. A day in the life You might start your day investigating a pipeline failure alert, tracing it back through Glue job logs to a schema change upstream. Later, you're in a design discussion with an applied scientist about what shape of data would best enable a new model, translating that into a concrete schema. In the afternoon, you're heads-down building a new ingestion adapter or reviewing a teammate's pull request. Your work directly determines what data is available, and reliable, for the platform's detection and reasoning capabilities. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Stop using Node.js like in 2020! 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