Data Engineer

Monaco
San Francisco, CA
On-site

Who this role is best for

Data engineers with production Spark pipeline experience and a comfort for ambiguity will find this high-autonomy, in-person role in the AI-native enterprise software domain.

Best fit for

  • Candidates with 2–6 years of experience in building and maintaining Spark pipelines on Databricks
    — “mid-level to senior engineer (roughly 2–6 years of experience)”
  • Individuals who have diagnosed pipeline failures and optimized for cost and performance
    — “the ability to explain how you diagnosed a real pipeline failure or bottleneck”
  • Professionals with strong SQL skills and a willingness to work closely with product teams
    — “Strong SQL. Comfort working as an individual contributor, directly with product teams”

Things to consider

  • This role requires a 5-day in-office commitment in San Francisco.
    — “We're an in-person team - 5 days in the office.”
  • The position emphasizes direct collaboration with product teams, not just backend work.
    — “working directly with product teams to turn what they need into reliable, cost-conscious data.”

How to stand out

  • Highlight specific instances of pipeline optimization and cost reduction in your resume.
    — “Diagnose performance and cost problems and fix their root causes.”
  • Emphasize your ability to translate product requirements into technical pipeline specs.
    — “Partner with product teams to turn analytics-product needs into pipeline requirements.”
  • Showcase experience with observability and monitoring tools in your interview prep.
    — “Set up monitoring and observability so pipeline reliability is measured, not assumed.”
Pace · Fast PacedCollaboration · HighAutonomy · HighDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Build, deploy, and operate production Spark data pipelines
  • Partner with product teams to turn analytics-product needs into pipeline requirements
  • Set up monitoring and observability
  • Diagnose performance and cost problems
Typical background
2-6 years of experience in data engineering

Skills & requirements

Required

Spark PipelinesDatabricksSQLData QualityMonitoring And ObservabilityCost-conscious Data

Preferred

Databricks EcosystemStreaming PipelinesLogical ReplicationAWS InfrastructurePlatform Administration

Stack & domain

SparkDatabricksSQLPythonCommunicationFinance

About the role

Original posting from Monaco via Ashby

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.

Source: Monaco careers (Ashby)

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