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DESCRIPTION

Worldwide Fulfillment by Amazon (WW FBA) empowers millions of sellers to scale globally through Amazon's leading fulfillment network. FBA sellers deliver fast, reliable Prime-eligible shipping and hassle-free returns to customers worldwide—enabling them to focus exclusively on business growth while Amazon handles operational logistics. The WW FBA Central Analytics team architects and maintains data infrastructure that delivers critical insights to WW FBA leadership. This team forms strategic partnerships across global product, program, and technology teams to unify datasets, implement self-service analytics platforms, and develop AI capabilities that transform raw data into insights.

We’re looking for a Senior Data Engineer who thrives on solving hard problems, shaping new capabilities, and delivering high-quality results in a fast-paced environment. You will be at the forefront of integrating LLM-powered solutions with robust backend systems, ensuring they scale securely and reliably to serve global customers.



Key job responsibilities
- Architect and implement a scalable, cost-optimized S3-based Data Lakehouse that unifies structured and unstructured data from disparate sources.
- Lead the strategic migration from our Redshift-centric architecture to a flexible lakehouse model.
- Establish metadata management with automated data classification and lineage tracking.
- Design and enforce standardized data ingestion patterns with built-in quality controls and validation gates.
- Architect a centralized metrics repository that becomes the source of truth for all FBA metrics.
- Implement robust data quality frameworks with staging-first policies and automated validation pipelines.
- Design extensible metrics schemas that support complex analytical queries while optimizing for AI retrieval patterns.
- Develop intelligent orchestration for metrics generation workflows with comprehensive audit trails.
- Lead the design of semantic data models that balance analytical performance with AI retrieval requirements.
- Implement cross-domain federated query capabilities with sophisticated query optimization techniques.
- Architect a globally distributed vector database infrastructure capable of managing billions of embeddings with consistent sub-100ms retrieval times.
- Design and implement hybrid search strategies combining dense vectors with sparse representations for optimal semantic retrieval.
- Establish automated compliance validation frameworks ensuring data handling meets Amazon's security standards.