Manager, Field Engineering - Global Telecommunications
Databricks Mosaic · Remote - Texas · New Jersey
CHECKED 14H AGO
Infrastructureremotesenior$219k–$301k
At a glance
Highlights
- Drive technical strategy for Databricks' most complex global telecom accounts.
- Own executive relationships guiding enterprise data and AI strategy.
- Lead architecture for multi-workload platforms and large-scale migrations.
What they offer
- performance bonus
- equity
- comprehensive benefits
Ideal candidate
Seasoned trusted advisor who thrives at the intersection of deep technical strategy and executive partnership. You care about scaling customer impact, mentoring others, and turning complex data challenges into business outcomes.
Requirements
- 8+ years in solutions architecture, principal engineering, technical pre-sales leadership, or a 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 patterns, data governance frameworks, and cloud-native platforms
- Expert-level Databricks Platform knowledge or equivalent depth in competing platforms
- Declared technical specialization with recognized depth
- Proven track record of driving consumption growth and platform adoption in large accounts
- Executive presence — ability to influence Directors, VPs, and C-level stakeholders
- Experience leading competitive displacements and complex enterprise evaluations
- Ability to travel to customers 30% of the time
- Strong strategic thinking connecting technical architecture decisions to business outcomes
- Bachelor's or Master's degree in Computer Science
- hiring process
- Live coding assessment and platform build during the interview.
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