Sr. Specialist Solutions Architect, Data, Aws

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

This role focuses on designing and building agent-ready data platforms that enable AI agents to autonomously process and analyze data. It involves advising on modern data strategies, including RAG and semantic search, and creating reference architectures for generative AI services. The role acts as a trusted advisor to customers, providing technical expertise in analytics, search, and streaming, and feeding customer feedback into AWS service roadmaps.

What you'd actually do

  1. Provide deep technical expertise in analytics, search, and streaming (Redshift, Athena, EMR, Glue, Kinesis, MSK, OpenSearch, Lake Formation, DataZone, S3 Tables, QuickSight, Clean Rooms) to advance AWS adoption across Japanese customers
  2. Design agent-ready data platforms, enabling AI agents to autonomously search, process, and analyse data through agentic workflows
  3. Advise on modern data strategies: lakehouse, Iceberg, zero-ETL, data mesh, RAG-based insight generation, and semantic search
  4. Lead architectural reviews, immersion days, PoC and builds
  5. Act as thought leader and trusted advisor from data engineers to C-suite

Skills

Required

  • Japanese language proficiency (N1+)
  • Business English skills
  • AWS data warehouse and reporting technologies (Redshift, Athena, S3)
  • Cross-functional collaboration
  • Deep hands-on experience with analytics technologies, data warehousing, data lakes, ETL/ELT pipelines, streaming, search, and data governance
  • Experience designing large-scale, production-grade data architectures

Nice to have

  • Experience designing AI/Agent-ready data platforms and modern data strategies (lakehouse, Iceberg, zero-ETL, RAG, semantic search, data mesh)
  • Track record building reusable assets (reference architectures, workshops, blogs) at scale
  • AWS certifications (SA, Data Analytics, ML Specialty)

What the JD emphasized

  • Deep hands-on experience with analytics technologies, data warehousing (e.g. Redshift, Snowflake, BigQuery, Teradata), data lakes, ETL/ELT pipelines, streaming (Kafka, Kinesis, Flink), search (Elasticsearch/OpenSearch), and data governance.
  • Experience designing large-scale, production-grade data architectures for enterprise customers, including considerations for performance, security, cost optimization, and operational excellence.

Other signals

  • design agent-ready data platforms
  • foundations that enable AI agents to autonomously search, process, and analyse data
  • semantic search powered by generative AI
  • RAG-based insight generation
  • build cloud-native and generative-AI-native reference architectures