Lead Data Engineer, Data Platform

About CrewAICrewAI 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 RoleYou'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 DoOwn and evolve CrewAI's data platform across ingestion, transformation, storage, semantic modeling, BI, and operational data qualityRationalize the existing data estate: product events, execution telemetry, OpenTelemetry-derived traces, application tables, Cube models, Redshift/data-lake tables, Metabase dashboards, and team-specific reportingEstablish 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 usageBuild and maintain the models, pipelines, and metric layers that make those numbers consistent across teamsPartner with product and engineering to improve instrumentation, event taxonomy, data contracts, and telemetry coverage for new featuresMake data self-serve through clear dashboards, documented datasets, reusable metric definitions, and sensible access patternsImprove reliability and trust in the stack through data quality checks, freshness monitoring, lineage, alerting, backfills, and incident/debug workflowsPartner with Discovery, product, and go-to-market teams on analysis behind recommendations, customer signals, usage patterns, and roadmap decisionsKeep the stack secure and cost-aware, including access control, PII handling, retention, and warehouse/query efficiencyHelp define how CrewAI uses data internally as the company scalesRequirementsWhat We're Looking ForStrong data engineering or analytics engineering experience, especially building data foundations in fast-moving product companiesExcellent SQL and data modeling skills, with experience designing reliable datasets, fact/dimension models, and metric definitionsExperience operating a warehouse or analytics store such as Redshift, Snowflake, BigQuery, Postgres, or similarFamiliarity with transformation and modeling tools such as dbt, Cube, semantic layers, or equivalent systemsExperience with event pipelines, product telemetry, application data, and BI tools such as Metabase, Looker, Mode, or similarStrong Python for data work, automation, validation, and operational workflowsProduct sense: you can turn ambiguous questions into useful metrics, and you care whether the numbers are understood correctlyPragmatism: you are comfortable inheriting messy systems, improving them incrementally, and choosing boring reliable solutions when they are rightStrong communication and documentation habits. You make data easier for other people to useComfort being the first dedicated owner in an early-stage, high-growth environmentBonusExperience with LLM, agent, observability, trace, usage, or cost analyticsExperience with OpenTelemetry, high-volume event data, or operational telemetryExperience with experimentation, causal analysis, activation/retention modeling, or customer health scoringExperience defining event taxonomies and instrumentation standards for SaaS productsFamiliarity with Rails/Postgres application data, background jobs, and product analytics in B2B SaaSLightweight ML or recommendation experience, especially where it supports product or customer workflows