Data Engineer II, WW FBA Central Analytics
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.
Locations
- India, KA, Bengaluru, Bengaluru, KA, India
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
25,000,000 - 45,000,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- - 3+ years of data engineering experienceintermediate
- - 4+ years of SQL experienceintermediate
- - Experience with data modeling, warehousing and building ETL pipelinesintermediate
Required Qualifications
- - 3+ years of data engineering experience (experience, 3 years)
- - 4+ years of SQL experience (experience, 4 years)
- - Experience with data modeling, warehousing and building ETL pipelines (experience)
- - 5+ years in data processing and AWS services (Kinesis, MSK, Lambda, Glue). (experience, 5 years)
- - Strong SQL, Python, and performance tuning expertise. (experience)
Preferred Qualifications
- - Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions (experience)
- - Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases) (experience)
- - Familiarity with global latency strategies (multi-region deployments, edge caching). (experience)
- - Experience in RAG or AI-powered analytics pipelines. (experience)
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.
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Data Engineer II, WW FBA Central Analytics
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.
Locations
- India, KA, Bengaluru, Bengaluru, KA, India
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
25,000,000 - 45,000,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- - 3+ years of data engineering experienceintermediate
- - 4+ years of SQL experienceintermediate
- - Experience with data modeling, warehousing and building ETL pipelinesintermediate
Required Qualifications
- - 3+ years of data engineering experience (experience, 3 years)
- - 4+ years of SQL experience (experience, 4 years)
- - Experience with data modeling, warehousing and building ETL pipelines (experience)
- - 5+ years in data processing and AWS services (Kinesis, MSK, Lambda, Glue). (experience, 5 years)
- - Strong SQL, Python, and performance tuning expertise. (experience)
Preferred Qualifications
- - Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions (experience)
- - Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases) (experience)
- - Familiarity with global latency strategies (multi-region deployments, edge caching). (experience)
- - Experience in RAG or AI-powered analytics pipelines. (experience)
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.