Data Engineer, Analytics

- 5+ years of SQL experience
- Experience with data modeling, warehousing and building ETL pipelines
- 5+ years in data engineering with experience in AWS and data quality tooling.
- Proficiency in SQL, Python, and workflow orchestration (MWAA/Airflow).
- Strong knowledge of data quality frameworks.
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
- Familiarity with LLM-driven metadata retrieval and semantic layer development.
- Experience with audit and lineage tools.

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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 seeking a Data Engineer II who will build the foundational data systems powering our LLM-based insights platform for Fulfillment by Amazon (FBA). This role focuses on implementing robust data standardization, governance frameworks, and metadata enrichment capabilities that ensure AI-generated outputs are consistently accurate and trustworthy. You will design and operationalize schema standards, lineage tracking systems, and quality validation frameworks that measurably reduce hallucinations and enhance retrieval precision.

Key job responsibilities
- Build dbt-based semantic models representing FBA metrics with business-friendly definitions consumed by RAG.
- Automate metadata harvesting with column-level descriptions, ownership tags, and business context for retrieval during text-to-SQL prompts.
- Implement lineage tracking tied to Redshift, S3, and Glue to power AI-driven source citations.
- Implement fine-grained access controls for embeddings and vector DB access, enforcing compliance .
- Build pipelines for proactive quality validation (null checks, distribution anomalies) feeding into AI's feedback loops.
- Partner with teams on metric standardization initiatives to avoid ambiguity in AI responses.