# Data Engineer at Monaco

Data Engineer at Monaco in San Francisco. On-site, full-time, paying $200k–$400k. Posted October 16, 2026.

- Company: Monaco
- Location: San Francisco
- Workplace: On-site
- Job type: Full-time
- Pay: $200k–$400k
- Experience: 2+ years
- Posted: 2026-10-16
- Apply: https://jobs.ashbyhq.com/monaco/74b30053-8a4b-4aae-abde-2861b0c5c458/application
- Source: https://jobmagic.ai/jobs/data-engineer-monaco-q0hghk

## Requirements

- 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.

## Description

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.

### **The role**

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).

### What you'll do

- 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.

### What you'll bring

- 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.

### Nice to have

- 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.

### **Location**

- San Francisco. We're an in-person team - 5 days in the office. At this stage, proximity genuinely accelerates product quality and team cohesion.