AmazonPosted 2mo ago

Sr. Specialist Solutions Architect, Data, AWS

Sr. Specialist Solutions Architect, Data, AWS at Amazon scores 75 out of 100 on AI centrality, which makes it AI Level 3 of 4 (Works on AI) on this board. The level measures how much of the work is AI, not seniority.

JP, 13, Tokyofull-time

AI in this role

bedrock

ragai-agents

In this role, as a Senior Specialist Solutions Architect, you will provide customers with best practices for building and operating analytics, search, and streaming workloads. Beyond that, you will partner with customers to design agent-ready data platforms, foundations that enable AI agents to autonomously search, process, and analyse data to support decision-making. This is an environment where you can take on the rapid pace of technological change head-on: from traditional BI/DWH to semantic search powered by generative AI, natural language data exploration, RAG-based insight generation, and the convergence of real-time streaming with AI.

While grounded in deep analytics expertise, you will approach customer challenges with a broad perspective that spans adjacent domains such as ML, security, and databases. As a trusted advisor, you will provide guidance not only on cost optimisation and performance, but also on new dimensions including data governance, AI/ML integration, and multimodal data utilisation, driving customer projects forward.

You will design and build cloud-native and generative-AI-native reference architectures with your own hands, and share them broadly with the technical community through white papers, workshops, blogs, and more. In addition to supporting customers in Japan, you will continuously provide feedback to shape the roadmap for AWS analytics, search, streaming, and generative AI services.

Data is the context layer that turns generic AI into personalised, differentiated customer experiences. Every AI conversation is a data conversation and this role sits at the centre of it for Japan. If you see the evolution of technology as an opportunity and are eager to co-create new value with customers through the fusion of data and AI, come join us and build the future together!

Key job responsibilities
• 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
• Design agent-ready data platforms, enabling AI agents to autonomously search, process, and analyse data through agentic workflows
• Advise on modern data strategies: lakehouse, Iceberg, zero-ETL, data mesh, RAG-based insight generation, and semantic search
• Lead architectural reviews, immersion days, PoC and builds
• Act as thought leader and trusted advisor from data engineers to C-suite
• Carry customer feedback and competitive signals back to service teams to shape the roadmap

A day in the life
You support customers across Japan's leading industries, Manufacturing, Financial Services, Telecommunications, Gaming, and Digital-Native companies. One morning you're whiteboarding a lakehouse architecture; midday you're in a CxO briefing making the case to migrate from on-premises Hadoop to a serverless analytics stack that enables agentic AI. The afternoon you're building a RAG-based PoC with OpenSearch and Bedrock, then feeding product feedback to the Redshift service team. You work with Japan's most recognised brands, helping them build the data platforms that power their next generation of AI-driven experiences.

About the team
The WWSO Data & AI team helps customers adopt our newest and most advantageous technologies. This role sits on the APJC Data team alongside Analytics, Database, and Storage Specialist SAs across six geos. In Japan, you work closely with the Analytics Specialists and Database/Storage SAs to deliver holistic data outcomes. We operate in a collaborative model where GTM Specialists, SAs, and account teams bring unique strengths in concert. If you thrive going deep on technology, love building for customers, and measure success by the architectures you've shaped — this is the role for you.

Basic qualifications

- Speak, write, and read fluently in Japanese at a business level or above (N1+)
- Experience in Business English skills, both verbal and written
- Experience with AWS data warehouse and reporting technologies like Redshift, Athena, S3, etc.
- Experience working cross-functionally with a wide breadth of stakeholders, users, engineers, and leaders to generate buy-in.
- 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.

Preferred qualifications

- 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) preferred

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.

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Skills and AI tools this role asks for

RagAI AgentsBedrock

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
How do you decide when an AI agent can act on its own versus asking for approval first?
What are the limits of Bedrock that you've run into, and how did you work around them?
Describe a typical day in a role like this one: which parts run through AI directly?
If you removed AI from this role, what would be left, and how do you decide what still needs a human?

Adapt your resume

List these exact terms on your resume: Rag, AI Agents, and Bedrock. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.

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