Analytics Solutions Architect

Amazon Amazon · Big Tech · 13, Japan +1 · Solutions Architect

Specialist Solutions Architect focused on designing and building agent-ready data platforms that leverage generative AI, RAG, semantic search, and real-time streaming for customers. The role involves providing best practices, designing reference architectures, and influencing service roadmaps.

What you'd actually do

  1. Provide customers with best practices for building and operating analytics, search, and streaming workloads.
  2. Partner with customers to design agent-ready data platforms — foundations that enable AI agents to autonomously search, process, and analyze data to support decision-making.
  3. Design and build cloud-native and generative-AI-native reference architectures.
  4. Share reference architectures broadly with the technical community through white papers, workshops, blogs, and more.
  5. Provide feedback to shape the roadmap for AWS analytics, search, streaming, and generative AI services.

Skills

Required

  • Experience building and nurturing relationships with internal and external stakeholders
  • Fluent spoken and written Japanese at business level or above (JLPT N1 or equivalent)
  • Deep experience in at least one of the following technical areas: big data processing technologies (Hadoop, Apache Spark, etc.), stream processing (Apache Kafka, Amazon Kinesis, Apache Flink, etc.), search platforms (OpenSearch, Elasticsearch, vector search, semantic search), data warehouse technical architectures, ETL, and reporting/analytics tools
  • Strong interest in and eagerness to learn about data architectures powered by generative AI (RAG, semantic search, vector databases, LLM orchestration, AI agent-driven data pipeline automation, etc.). Hands-on experience in designing or building such architectures is a plus

Nice to have

  • 3+ years of experience in cloud architecture and solution implementation, or an AWS Associate-level certification
  • Deep experience in at least one of the following technical areas: generative AI / AI agent platforms, AI/ML, databases, governance, and storage
  • Experience in a customer-facing role, collaborating with customer executives, engineers, or partners to solve business challenges using cutting-edge technologies
  • Demonstrated written and verbal communication skills in English, with the ability to engage stakeholders at all levels

What the JD emphasized

  • generative AI
  • AI agents
  • data platforms
  • analytics
  • search
  • streaming
  • RAG
  • semantic search
  • vector databases
  • LLM orchestration
  • AI agent-driven data pipeline automation

Other signals

  • designing agent-ready data platforms
  • AI agents autonomously search, process, and analyze data
  • semantic search powered by generative AI
  • RAG-based insight generation
  • convergence of real-time streaming with AI
  • AI/ML integration
  • multimodal data utilization