Senior Software Engineer — Data Lakehouse & AI

Amazon Inc.

Redmond

On-site

AUD 244,000 - 331,000

Full time

2 days ago

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Benefits offered by this job

RSUs

Health insurance

401k matching

Paid time off

Parental leave

Job summary

Amazon Leo seeks an experienced Software Development Engineer to build cloud services and APIs for the Leo AI Foundations—enabling intelligent software operation across satellites, gateways, and customer terminals.

You will deliver low-latency, highly scalable architectures critical to delivering fast internet and AI capabilities for global connectivity. Lead design, Drive reliable data platforms and security compliance.

Qualifications

5+ years of non-internship professional software development experience.
5+ years of programming with at least one software programming language experience.
5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience.
Experience as a mentor, tech lead or leading an engineering team.

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.