Fulfillment by Amazon (FBA) enables sellers to scale their businesses globally by leveraging Amazon’s world-class fulfillment network. Sellers using FBA benefit from fast, reliable shipping, Prime delivery eligibility, and hassle-free returns—allowing them to focus on growth while we handle operations. The WW FBA Central Analytics team builds and operates scalable, enterprise-grade data infrastructure, tools, and analytics solutions that power WW FBA business. We partner across global product, program, and operations teams to unify diverse datasets, deliver self-service analytics, and develop next-generation capabilities using LLMs to unlock insights.
Our charter includes building the foundational pipelines, governance frameworks, and intelligent interfaces that enable internal customers to query, analyze, and act on complex datasets with natural language. This is an opportunity to work on one of the largest, complex, and critical analytics ecosystems, designing solutions that combine massive scale, high reliability, and advanced AI.
We are seeking a Data Engineer I who will contribute to a GenAI-powered insights assistant initiative by developing and maintaining performant ETL pipelines and semantic layers for WW FBA metrics. Your work ensures the assistant’s text-to-SQL capabilities deliver accurate, up-to-date structured data insights supporting natural-language query responses.
Key job responsibilities
- Develop and maintain ETL pipelines in Spark to transform and load FBA metrics into the Data Lakehouse.
- Optimize SQL queries for fast, cost-efficient access by AI systems.
- Support dbt semantic models and automate metadata enrichment with Glue.
- Write automated tests to ensure data quality and freshness.
- Build pipelines that add business context metadata to help AI generate accurate SQL queries.- 1+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
- Experience with one or more scripting language (e.g., Python, KornShell)- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.
- Proficiency in dbt, Airflow/MWAA, AWS Glue, Kinesis.
- Experience building semantic layers or BI models.
- Familiarity with prompt-driven SQL generation and AI-assisted query validation.
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