Sr. Solutions Architect - Agencies
Databricks Mosaic · Northeast - United States · Remote - Virginia
CHECKED 15H AGO
Infrastructureremotesenior$219k–$301k
At a glance
Highlights
- Lead architecture for multi-workload platforms, migrations and AI at scale.
- Own executive relationships guiding enterprise data and AI strategy.
- Drive consumption strategy influencing platform adoption and revenue growth.
What they offer
- performance bonus
- equity
- comprehensive benefits and perks
Ideal candidate
A strategic technical leader who thrives on complex enterprise challenges and executive influence. You mentor others, connect architecture to business outcomes, and are passionate about scaling data and AI impact.
Requirements
- 8+ years in solutions architecture, principal engineering, technical pre-sales leadership, or senior customer-facing technical role
- Expert-level coding proficiency — Python, SQL, and ideally Spark/PySpark
- Deep expertise in enterprise data architecture: distributed systems at scale, streaming/real-time, lakehouse design, governance and cloud-native platforms
- Expert-level Databricks Platform knowledge or equivalent depth in competing platforms
- Declared technical specialization with recognized depth, e.g. real-time, ML/AI, governance, migrations
- Proven track record driving consumption growth and platform adoption in large accounts
- Executive presence influencing Directors, VPs, and C-level on data and AI strategy
- Experience leading competitive displacements and complex enterprise evaluations
- 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