Data Engineer

- 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
- 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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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.


This position can be located in LUX21 or LHR16.

Key job responsibilities
Responsibilities include but are not limited to:

- 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.