Lead Data Engineer (Data Platform)

Join CrewAI as a Lead Data Engineer and take ownership of our data platform. You will be responsible for rationalizing the existing data estate, establishing trusted source-of-truth metrics, and making data self-serve. You will also partner with product and engineering teams to improve instrumentation and telemetry coverage, and help define how CrewAI uses data internally as the company scales. This is a remote-first position with strong ownership and impact opportunities.

• Own and evolve CrewAI’s data platform across ingestion, transformation, storage, semantic modeling, BI, and operational data quality.
• Rationalize the existing data estate, establish trusted source-of-truth metrics for the business and product, and build and maintain the models, pipelines, and metric layers.
• Partner with product and engineering to improve instrumentation, event taxonomy, data contracts, and telemetry coverage for new features.
• Comfort being the first dedicated owner in an early-stage, high-growth environment
• 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
• Strong data engineering or analytics engineering experience, especially building data foundations in fast-moving product companies
• Strong communication and documentation habits. You make data easier for other people to use
• Experience with event pipelines, product telemetry, application data, and BI tools such as Metabase, Looker, Mode, or similar
• Familiarity with transformation and modeling tools such as dbt, Cube, semantic layers, or equivalent systems
• Experience with OpenTelemetry, high-volume event data, or operational telemetry
• Experience with experimentation, causal analysis, activation/retention modeling, or customer health scoring
• Excellent SQL and data modeling skills, with experience designing reliable datasets, fact/dimension models, and metric definitions
• Experience with LLM, agent, observability, trace, usage, or cost analytics
• Experience operating a warehouse or analytics store such as Redshift, Snowflake, BigQuery, Postgres, or similar
• Pragmatism: you are comfortable inheriting messy systems, improving them incrementally, and choosing boring reliable solutions when they are right
• Experience defining event taxonomies and instrumentation standards for SaaS products
• Lightweight ML or recommendation experience, especially where it supports product or customer workflows
• Familiarity with Rails/Postgres application data, background jobs, and product analytics in B2B SaaS