Data Platform Engineer
San Francisco · San Francisco, California, United States
- Pay
- Pay not listedSimilar roles state $127K–$190K
- Work setting
On-site — work setting source
Location type: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Engineering — team source
Department: Engineering
From the employer’s posting
Before you apply
- Sponsorship
Visa sponsorship not confirmed — sponsorship source
Will you now or in the future require visa sponsorship to work in the United States?
From the employer’s application form
What you’ll work on
Full postingBuild scalable pipelines and event-driven systems for ingesting, transforming, and serving data
Support ML workflows such as training data, evaluation, embeddings and feature pipelines
Solve distributed systems challenges around reliability, latency, consistency and scale
Improve observability, tooling and developer experience for data, ML and agent systems
From the employer’s posting
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
All qualificationsCore experience
- 5+ years building data platforms, ML infrastructure, or backend systems
- Deep experience with technologies like PostgreSQL, Redis, Celery, Temporal, Kafka or Spark
- Ability to lead major architecture decisions and execute on them quickly
- Experience maintaining and scaling production systems through rapid workload growth
Qualification wording
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
Tools in this posting
- Databricks
- Elasticsearch
- Kafka
- PostgreSQL
- Redis
- Snowflake
- Spark
Source — Tool mentions in context
- 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.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Aug 12, 2026 · 78 days ago
- Recorded sightings
- 67
- Last seen by us
- Oct 30, 2026
- Employer says posted
- Feb 10, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
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Job 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
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.
Source details
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
San Francisco, California, United States
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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