Lead Data Engineer, Data Platform
CrewAI · San Francisco, California, United States · Posted Jul 9, 2026
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About CrewAI
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.
The Role
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.
What You’ll Do
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.