Incident Manager
Databricks Mosaic · United States · Remote - Texas
CHECKED 15H AGO
Infrastructureremotesenior$145k–$199k
At a glance
Highlights
- Lead Databricks’ most critical production incidents to restore operations fast.
- Own customer and executive communications during high-impact events.
- Drive root-cause analysis to prevent failures and improve resilience.
What they offer
- Performance bonus
- Equity
- Comprehensive benefits
Ideal candidate
A calm, decisive operator with 5+ years in SRE or production ops who thrives under pressure, communicates clearly with executives and customers, and cares deeply about resilience and continuous improvement.
Requirements
- 5+ years of experience in incident management, site reliability engineering, or production operations supporting large-scale, cloud-native systems
- Proven ability to lead and coordinate high-severity incidents, including identifying impact, isolating fault domains, and managing multi-team response efforts
- Strong understanding of cloud infrastructure (AWS, Azure, or GCP) including compute, networking, storage, and observability
- Deep expertise in log analysis and debugging with log aggregation tools like Datadog, Elasticsearch, Splunk, Cloud Logging, or OpenTelemetry
- Hands-on experience with observability systems for metrics, logging, and tracing such as Prometheus, Grafana, OpenTelemetry
- Proficiency in at least one programming or scripting language (Python, Go, or Bash) for automating diagnostics and analysis
- Experience developing and maintaining incident playbooks and communication templates
- Excellent writing skills with ability to summarize and communicate to technical and business audiences
- BS, Master's or other advanced degree in Computer Science, Computer Engineering, or related Engineering field
- remote policy
- Remote within US
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