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San Francisco, California, United States
Onsite
Monaco is building an AI-native revenue platform that replaces the fragmented GTM stack - CRM, sequencing, call recording, enrichment, pipeline management with one unified system, consolidating 6–10 disconnected tools into a single platform built for the AI era.
We launched publicly in Feb 2026, are already 65 people, and have strong early product-market fit generating millions in ARR within months of launch. You'll have the chance to both scale our core systems and build new features from 0 to 1.
We've raised $85M in our Series B from Founders Fund, Benchmark, and Human Capital, and our founders previously led Brex, Apollo, and Clari.
Come join us if you want to be part of a high autonomy, high pace team reinventing one of the biggest categories in enterprise software.
You'll own the Spark pipelines on Databricks that power Monaco's analytics products, working directly with product teams to turn what they need into reliable, cost-conscious data. This is a hands-on individual contributor role for a mid-level to senior engineer (roughly 2–6 years of experience).
Build, deploy, and operate production Spark data pipelines on Databricks.
Partner with product teams to turn analytics-product needs into pipeline requirements, including data quality and freshness expectations.
Set up monitoring and observability so pipeline reliability is measured, not assumed.
Diagnose performance and cost problems and fix their root causes.
Recommend pragmatic standards for running pipelines as our data and platform scale.
Hands-on experience building, deploying, and maintaining production Spark pipelines.
Deep understanding of how concepts like clustering, shuffles, different join strategies, and batch versus stream processing create tradeoffs in performance, freshness, and cost.
Experience with reliability, monitoring, and observability, and the ability to explain how you diagnosed a real pipeline failure or bottleneck.
Strong SQL.
Basic programming skills in any language (Python is a plus).
Comfort working as an individual contributor, directly with product teams, and through ambiguity.
Familiarity with the Databricks ecosystem.
Experience with streaming pipelines and logical replication, such as replicating Postgres into Databricks.
Experience building RAG pipelines.
AWS infrastructure experience.
Platform administration experience: roles, permissions, access controls, and visibility management.
San Francisco. We're an in-person team - 5 days in the office. At this stage, proximity genuinely accelerates product quality and team cohesion.
