Amazon

Data Engineer II, WW FBA Central Analytics

Posted On

Sept. 19, 2025

Location

Bengaluru, Karnataka

Experience Level

Freshers/Experienced

Job Type

Data Science

Apply By

Oct. 28, 2025

Job 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 seeking a Data Engineer II who will own the near real-time data infrastructure powering our LLM-based insights platform for WW FBA. This role focuses on building high-performance streaming pipelines, optimizing embedding freshness, and implementing global latency strategies to ensure we deliver up-to-date, low-latency insights worldwide. You will play critical role in scaling AI-driven analytics across multiple regions while balancing performance and cost.

Key job responsibilities
- Design and implement streaming data pipelines to process high-volume, near real-time data from multiple sources.
- Build and maintain the infrastructure supporting large language models, including embedding generation, vector storage, and retrieval systems.
- Develop and optimize a modern data lakehouse to support both batch and real-time analytics workloads.
- Implement caching strategies, query optimization, and multi-region deployment to achieve sub-second response times.
- Balance performance requirements with cost considerations through efficient resource utilization and workload optimization.
- Ensure data reliability, freshness, and compliance across the entire data pipeline.- 3+ years of data engineering experience
- 4+ years of SQL experience
- Experience with data modeling, warehousing and building ETL pipelines
- 5+ years in data processing and AWS services (Kinesis, MSK, Lambda, Glue).
- Strong SQL, Python, and performance tuning expertise.

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