Lead Data Engineer, Data Platform

crewAI•San Francisco, CA

About The Position

CrewAI is seeking its first dedicated data engineering hire to own and evolve the company's data platform. This role is crucial for transforming scattered data into a coherent, trusted, and useful foundation. The focus will be on data infrastructure and analytics engineering, including pipelines, warehouse/lake design, semantic modeling, metric definitions, data quality, and self-serve access. The individual will also be responsible for translating complex questions into clear analyses and reliable dashboards to inform product decisions. This is a foundational role with significant impact in a high-growth environment.

Requirements

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: ability to turn ambiguous questions into useful metrics and ensure correct understanding of data.
Pragmatism: comfort inheriting messy systems, improving them incrementally, and choosing reliable solutions.
Strong communication and documentation habits.
Comfort being the first dedicated owner in an early-stage, high-growth environment.

Nice To Haves

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.

Responsibilities

Own and evolve CrewAI’s data platform across ingestion, transformation, storage, semantic modeling, BI, and operational data quality.
Rationalize the existing data estate, including 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, covering 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 to ensure consistency of numbers 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 related to 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.