Description Amazon Leo is Amazon’s low Earth orbit satellite network. Our mission is to deliver fast, reliable internet connectivity to customers beyond the reach of existing networks. From individual households to schools, hospitals, businesses, and government agencies, Amazon Leo will serve people and organizations operating in locations without reliable connectivity. This role is for a Data Engineer who will design, implement, and operate globally distributed systems that enable Leo to achieve low single-digit-second query responses within a near real-time analytics layer or lakehouse, and to support agentic AI capabilities on top. You’ll build these systems using the latest AWS technologies and best-in-industry data engineering practices. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. Key job responsibilities - Architect and implement a scalable, cost-optimized S3-based Data Lakehouse that unifies structured and unstructured data from disparate sources. - Architect and implement a scalable, cost-performance-optimized OLAP-based analytics layer - 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 Leo 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. A day in the life This role is for a Data Engineer who will build new cloud services and APIs that facilitate and orchestrate the Leo AI Foundations—enabling intelligent software operation across Leo devices such as satellites, ground gateways, and customer terminals. You will design and deliver low-latency, highly scalable architectures that are critical to providing high-quality internet service and AI capabilities to customers. About the team Leo AI Foundations builds the intelligent cloud backbone that powers AI-driven decision making across Amazon’s Leo constellation — from satellites and ground gateways to customer terminals. Our team designs and operates large-scale data and compute systems that enable training, inference, and agentic intelligence for optimizing network performance, routing, and user experience in real time. We combine expertise in distributed systems, data lakehouse architectures, and applied machine learning to deliver scalable, low-latency AI capabilities that integrate seamlessly with Leo's software-defined space and ground systems. We move fast, innovate boldly, and work across boundaries to make global connectivity smarter and more efficient. Basic Qualifications - 3+ years of data engineering experience - 3+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience - 3+ years of developing and operating large-scale data structures for business intelligence analytics using OLAP technologies experience - 3+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience - 3+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience - 3+ years of developing and operating large-scale data structures for business intelligence analytics using Oracle experience - Experience with data modeling, warehousing and building ETL pipelines Preferred Qualifications - 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) Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $118,900/year in our lowest geographic market up to $205,600/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits . This position will remain posted until filled. Applicants should apply via our internal or external career site.