SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Monaco is an AI-native revenue platform consolidating 6–10 disconnected GTM tools (CRM, sequencing, call recording, enrichment, pipeline management) into a single unified system. The company launched publicly in February 2026, has 65 employees, and has achieved strong early product-market fit generating millions in ARR within months of launch. Monaco raised $85M in Series B funding from Founders Fund, Benchmark, and Human Capital, with founders from Brex, Apollo, and Clari.
In this role, you will own the Spark pipelines on Databricks that power Monaco's analytics products. You'll work directly with product teams to translate their analytics needs into reliable, cost-conscious data infrastructure. This is a hands-on individual contributor position for a mid-level to senior engineer.
Key responsibilities include:
- Build, deploy, and operate production Spark data pipelines on Databricks
- Partner with product teams to define analytics-product requirements, including data quality and freshness expectations
- Implement monitoring and observability to measure pipeline reliability
- Diagnose and resolve performance and cost issues at their root cause
- Recommend pragmatic standards for pipeline operations as data and platform scale
The team operates in-person in San Francisco, 5 days per week, with the company emphasizing that proximity accelerates product quality and team cohesion at this stage.
REQUIREMENTS:
- Hands-on experience building, deploying, and maintaining production Spark pipelines
- Deep understanding of clustering, shuffles, join strategies, and batch vs. stream processing tradeoffs (performance, freshness, cost)
- Experience with reliability, monitoring, and observability; ability to explain how you diagnosed a real pipeline failure or bottleneck
- Strong SQL skills
- Basic programming skills in any language (Python preferred)
- Comfort working as an individual contributor, directly with product teams, and navigating ambiguity
- Estimated experience level: 2–6 years
NICE TO HAVE:
- Familiarity with the Databricks ecosystem
- Experience with streaming pipelines and logical replication (e.g., Postgres to Databricks)
- Experience building RAG pipelines
- AWS infrastructure experience
- Platform administration experience (roles, permissions, access controls, visibility management)
