Data Engineer

Monaco • San Francisco, CA

8d • $200,000 - $400,000 • Onsite

About The Position

Monaco is building an AI-native revenue platform that unifies tools such as CRM, sequencing, call recording, enrichment, and pipeline management. The company launched publicly in February 2026 and has 65 people, with early product-market fit and millions in ARR within months of launch. It has raised $85 million in a Series B from Founders Fund, Benchmark, and Human Capital. The founders previously led Brex, Apollo, and Clari.

The Data Engineer will own the Spark pipelines on Databricks that power Monaco’s analytics products. This is a hands-on individual-contributor role for a mid-level to senior engineer, working directly with product teams to translate their needs into reliable, cost-conscious data. The role offers the opportunity to scale core systems and build new features from 0 to 1.

The position is based in San Francisco and requires working in the office five days per week. Monaco describes itself as a high-autonomy, high-pace team.

Requirements

• Hands-on experience building, deploying, and maintaining production Spark pipelines.
• Deep understanding of how clustering, shuffles, join strategies, and batch versus stream processing affect performance, freshness, and cost.
• Experience with reliability, monitoring, and observability, including the ability to explain how a real pipeline failure or bottleneck was diagnosed.
• Strong SQL skills.
• Basic programming skills in any language; Python is a plus.
• Comfort working as an individual contributor, directly with product teams, and through ambiguity.
• Roughly 2–6 years of experience, as indicated for the target mid-level to senior engineer.

Nice To Haves

• 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, including roles, permissions, access controls, and visibility management.

Responsibilities

• Build, deploy, and operate production Spark data pipelines on Databricks.
• Partner with product teams to translate analytics-product needs into pipeline requirements, including data quality and freshness expectations.
• Set up monitoring and observability to measure pipeline reliability.
• Diagnose performance and cost problems and address their root causes.
• Recommend pragmatic standards for running pipelines as data and the platform scale.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed