Data Platform Engineer

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. We’re consolidating 6–10 disconnected tools into a single, purpose-built platform and redefining what’s possible when all the data lives under one roof in the age of AI - this is a category-defining shift, not an incremental improvement.

We launched publicly in Feb 2026 and are 50 people and growing. We have strong early product-market fit, creating millions in ARR in only a few months post-launch. Opportunities exist in both scaling core systems and workflows, and building new cutting edge features from 0 to 1.

We’ve raised $85M through our Series B from legendary investors including Founders Fund, Benchmark, and Human Capital. Our founders are industry veterans who previously led companies like 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.

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 we’re looking for:

• 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.