Lead Data Engineer, Data Platform

CrewAI is the leading framework and enterprise platform for building and orchestrating multi-agent AI systems, powering 300M+ agent executions per month across thousands of companies. As the product, platform, and customer base scale, data is becoming one of the most important systems in the company: how we understand usage, reliability, activation, customer health, cost, governance, and where to invest next.

Today, we have meaningful data already, but it is spread across product telemetry, trace data, application databases, analytics tables, Cube models, Metabase dashboards, and team-specific queries. We need someone to turn that into a coherent, trusted, useful data foundation.

You’ll be CrewAI’s first dedicated data engineering hire. Your job is to own the data foundation end to end: rationalize what exists, improve the infrastructure, define trusted metrics, close instrumentation gaps, and make data accessible enough that product, growth, engineering, customer success, and leadership can actually use it.

This is a foundational role with real range. The center of gravity is data infrastructure and analytics engineering: pipelines, warehouse/lake design, semantic modeling, metric definitions, data quality, and self-serve access. You’ll also be the person who turns messy questions into clear analysis, reliable dashboards, and better product decisions.

This is not a maintenance role. It is a “make data legible and useful for the company” role.

• Own and evolve CrewAI’s data platform across ingestion, transformation, storage, semantic modeling, BI, and operational data quality.
• Rationalize the existing data estate: product events, execution telemetry, OpenTelemetry-derived traces, application tables, Cube models, Redshift/data-lake tables, Metabase dashboards, and team-specific reporting.
• Establish trusted source-of-truth metrics for the business and product, including executions, active builders/users, activation, deployment health, token and cost usage, customer health, governance adoption, retention, and feature usage.
• Build and maintain the models, pipelines, and metric layers that make those numbers consistent across teams.
• Partner with product and engineering to improve instrumentation, event taxonomy, data contracts, and telemetry coverage for new features.
• Make data self-serve through clear dashboards, documented datasets, reusable metric definitions, and sensible access patterns.
• Improve reliability and trust in the stack through data quality checks, freshness monitoring, lineage, alerting, backfills, and incident/debug workflows.
• Partner with Discovery, product, and go-to-market teams on analysis behind recommendations, customer signals, usage patterns, and roadmap decisions.
• Keep the stack secure and cost-aware, including access control, PII handling, retention, and warehouse/query efficiency.
• Help define how CrewAI uses data internally as the company scales.

• Strong data engineering or analytics engineering experience, especially building data foundations in fast-moving product companies.
• Excellent SQL and data modeling skills, with experience designing reliable datasets, fact/dimension models, and metric definitions.
• Experience operating a warehouse or analytics store such as Redshift, Snowflake, BigQuery, Postgres, or similar.
• Familiarity with transformation and modeling tools such as dbt, Cube, semantic layers, or equivalent systems.
• Experience with event pipelines, product telemetry, application data, and BI tools such as Metabase, Looker, Mode, or similar.
• Strong Python for data work, automation, validation, and operational workflows.
• Product sense: you can turn ambiguous questions into useful metrics, and you care whether the numbers are understood correctly.
• Pragmatism: you are comfortable inheriting messy systems, improving them incrementally, and choosing boring reliable solutions when they are right.
• Strong communication and documentation habits. You make data easier for other people to use.
• Comfort being the first dedicated owner in an early-stage, high-growth environment.

• Experience with LLM, agent, observability, trace, usage, or cost analytics.
• Experience with OpenTelemetry, high-volume event data, or operational telemetry.
• Experience with experimentation, causal analysis, activation/retention modeling, or customer health scoring.
• Experience defining event taxonomies and instrumentation standards for SaaS products.
• Familiarity with Rails/Postgres application data, background jobs, and product analytics in B2B SaaS.
• Lightweight ML or recommendation experience, especially where it supports product or customer workflows.