Solutions Architect (Pre-sales) – Manufacturing & Automotive
Databricks Mosaic · Tokyo, Japan
CHECKED 15H AGO
Infrastructureonsite
At a glance
Highlights
- Partner with Japan's leading automotive makers to drive measurable business outcomes.
- Turn connected-vehicle, quality and smart-factory challenges into AI solutions.
- Build hands-on prototypes and demos that accelerate customer confidence and adoption.
Ideal candidate
A builder-minded pre-sales architect passionate about automotive and manufacturing who thrives translating complex business challenges into impactful data and AI outcomes and influencing stakeholders from engineers to C-level.
Requirements
- Business-professional Japanese required; business-level English preferred
- Experience in technical pre-sales, consulting, technical architecture, or equivalent role with complex customers and sales lifecycles
- Technical background in data engineering, data warehousing, machine learning, artificial intelligence, or related fields
- Experience designing and implementing solutions on public cloud platforms including AWS, Azure, or GCP
- Hands-on expertise delivering complex proof-of-concepts and technical demonstrations
- Ability to code in Python or SQL with willingness to learn Apache Spark; Scala, Java, or R beneficial
- Strong understanding of enterprise data platforms, distributed data systems, analytics, machine learning, generative AI, or modern cloud architectures
- Experience coaching or mentoring technical professionals or account teams
- Databricks or relevant cloud and data certifications
- hiring process
- Recruiter Screen → Hiring Manager Screen → Design and Architecture Interview → Vibe Coding Interview → Build, Demo, Pitch! Presentation → Reference Check
Lab profile
About Databricks Mosaic
Builds unified data analytics and AI platform to productionize AI applications.
What they build
- Databricks platform
- Unified Data Analytics and AI
- AI platform
Known for
- Forward-deployed AI engineering
- Productionizing first-of-kind AI applications
- Customer platform adoption and scale
- Cross-functional engineering and product collaboration