Sr. Partner Solutions Architect - Customer Lake & Lakewatch
Databricks Mosaic · United States · Remote - California
Infrastructureremotesenior$219k–$301k
At a glance
Highlights
- Scale partner practices to drive Customer Lake adoption across SIs and ISVs.
- Build reusable blueprints to accelerate enterprise Customer Lake rollouts.
- Guide partners deploying Lakewatch for automated data quality and health telemetry.
What they offer
- Annual performance bonus
- Equity
- Comprehensive benefits and perks
Ideal candidate
Expert-level technical leader who thrives enabling partners and influencing executives, passionate about scaling data platforms through repeatable practices and business-aligned architecture.
Requirements
- 8+ years in solutions architecture, partner technical management, principal engineering, or senior pre-sales roles
- Proven track record working with system integrators, cloud providers, or strategic technology partners
- Expert-level coding proficiency — Python, SQL, and ideally Spark/PySpark
- Deep expertise in enterprise data architecture, distributed systems, real-time/streaming and governance
- Deep knowledge of data observability, automated data quality, and platform telemetry
- Expert-level Databricks Platform knowledge or equivalent depth with rapid learning ability
- Proven track record driving consumption growth and platform adoption in large accounts
- Executive presence to influence VPs, Practice Directors, and C-level stakeholders
- Experience leading competitive displacements and complex enterprise evaluations
- Bachelor's or Master's in Computer Science, Engineering, or quantitative discipline or equivalent
- 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
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