Data Platform Engineer
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- Work arrangement
- onsite
- Employment
- FullTime
- Stated compensation
- USD 200,000–400,000 1 YEAR
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Requirements
1 with evidence8 unclear or unstated- Remote policy
- onsite
“Provider workplace type: onsite”
From employer posting metadata - Employment type
- FullTime
- Compensation
- USD 200,000–400,000 1 YEAR
8 fields not stated in this posting
- Employer sponsorship
- Not specified
- Geographic scope
- Not specified
- Degree requirement
- Not specified
- Security clearance
- Not mentioned
- Timezone / hours
- Not specified
- Citizenship requirement
- Not mentioned
- Minimum experience (years)
- Not specified
- Work authorization
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Original job description
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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
We're looking for a Data Platform Engineer to help build Monaco's data and ML platform - the pipelines, context systems, and infrastructure that power our AI-driven product. You'll work on the foundation that makes models, agents, and workflows actually useful in production.
This is a high-ownership role at the intersection of data engineering, distributed systems, and applied AI.
What You'll Do
Build scalable pipelines and event-driven systems for ingesting, transforming, and serving data.
Support ML workflows: training data, evaluation, embeddings, feature pipelines.
Solve distributed systems challenges around reliability, latency, consistency, and scale.
Improve observability, tooling, and developer experience for data, ML, and agent systems.
What You'll Bring
5+ years building data platforms, ML infrastructure, or backend systems.
Deep experience in technologies like PostgreSQL, Redis, Celery, Temporal, ElasticSearch or Turbopuffer, Kafka, Spark, Databricks or Snowflake, etc.
Ability to lead major architecture decisions and execute on them fast, maintain and scale production systems through rapid workload growth.
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.
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