Lead Data Engineer, CrewAI

• 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

Desirable

• 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