Data Engineer, Amazon Customer Service Data Analytics Support Hub

Amazon's Customer Service (CS) department is seeking an experienced Data Engineer to join the Data Analytics Support Hub (DASH) Advanced Analytics team. CS is the heart of Amazon; our vision is to be Earth's most customer-centric company. The successful candidate will be a key member of the Advanced Analytics branch within DASH, which is evolving Q&E from descriptive ("what happened") to diagnostic and predictive analytics at worldwide

As a Data Engineer II, you will build and own the production data infrastructure that turns Q&E domain expertise into scalable diagnostic and predictive analytics. You will own end-to-end delivery of pipelines for multi-contact journey analytics (Transfers, Repeats, DART, ECR/VPI), transcripts ingestion at WW scale, LLM-serving datasets, and model-serving feature tables. You will also serve as the bridge that connects central data platforms to Q&E-specific, multi-contact journey use cases that central single-touchpoint platforms do not address., Build and own production data pipelines for Q&E diagnostic and predictive workloads: transcripts ingestion at WW scale, multi-contact threading, journey-grain feature tables, and model-serving datasets.
- Integrate Q&E pipelines with central infrastructure: consume data and tooling, and connect to Data Stream Service (DSS) to move from 24-36 hour validation latencies toward semi real-time signals.
- Own end-to-end data models and pipelines for the new KPI portfolio
- Scale innovations from prototypes into maintainable, certified production pipelines.
- Build the transcripts prototyping infrastructure used by BIEs and Data Scientists: scalable, secure, App-Security-red-certified, with templates and tooling that reduce new ad-hoc request time-to-delivery.
- Productionize LLM-based diagnostic outputs into reliable datasets that power WBR "why" automation and self-service dive-deeps.
- Own dataset quality, lineage, freshness SLAs, backfills, and alerting. Drive Shepherd risk remediation, App Security reviews, Kale, Legal, Threat Models, DI diagrams, and ASR certification for production deployment.
- Perform code reviews in CRUX; follow Brazil/Apollo/Pipelines best practices; contribute to DE engineering standards and the reusable-components knowledge repository.

Experience in data engineering
- Experience with data modeling, warehousing and building ETL pipelines
- Master's degree in computer science, engineering, analytics, mathematics, statistics, IT or equivalent
- Experience as a data engineer or related specialty (e.g., software engineer, business intelligence engineer, data scientist) with a track record of manipulating, processing, and extracting value from large datasets
- Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence

Preferred Qualifications

- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience working on and delivering end to end projects independently
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS

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