Solutions Architect (Digital Native Business)
Databricks Mosaic · London, United Kingdom
CHECKED 14H AGO
Infrastructureonsitesenior
At a glance
Highlights
- Own end-to-end technical strategy from discovery to production deployment and growth.
- Design scalable production-grade solutions across data engineering, ML and AI.
- Act as trusted advisor to customer architects while influencing product direction.
Ideal candidate
A strategic yet hands-on architect who thrives advising senior technical leaders and turning complex data challenges into production-scale solutions. You enjoy owning customer outcomes and growing deep expertise in a specialist domain.
Requirements
- 6+ years in solutions architecture, data engineering, technical pre-sales, or senior hands-on technical role
- Strong coding proficiency in Python and SQL with live coding, debugging and solution-building skills
- Deep expertise in distributed data systems, scalable pipelines, streaming and lakehouse patterns
- Proficient on Databricks Platform with developing specialisation in areas like streaming, ML/AI or governance
- Proven ability to lead architecture discussions with senior stakeholders including whiteboarding and design reviews
- Experience with production deployments on AWS, Azure or GCP including infrastructure, security and governance
- Track record of driving platform adoption and consumption growth within accounts
- Excellent communication skills translating complex architectures into business value
- Bachelor's or Master's in Computer Science, Engineering or quantitative discipline or equivalent experience
- hiring process
- Recruiter Screen → Hiring Manager Screen → Design and Architecture Interview → Live Coding Assessment → 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