Lead Data Engineer, Data Platform

crewAI, Inc.; San Francisco (CA); On-site; USD 140,000 - 230,000; Full time

Job summary

CrewAI is seeking its first dedicated data engineer to own the data foundation end-to-end, rationalize what exists, and make data accessible for product, growth, engineering, and leadership.

The role centers on data infrastructure, pipelines, semantic modeling, metric definitions, data quality, and self-serve analytics, turning messy questions into clear analyses and informed product decisions.

Qualifications

• Strong data engineering or analytics engineering experience in product companies.
• Excellent SQL and data modeling skills with reliable datasets.
• Experience with analytics warehouses like Redshift, Snowflake, BigQuery, or Postgres.
• Familiarity with 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: turn ambiguous questions into useful metrics and ensure numbers are understood.
• Pragmatism: improve messy systems incrementally with reliable solutions.
• Strong communication and documentation habits.
• Comfort being the first dedicated owner in an early-stage, high-growth environment.

Responsibilities

• Own and evolve data platform across ingestion, transformation, storage, semantic modeling, BI, and operational data quality.
• Rationalize the existing data estate: product events, execution telemetry, traces, application tables, models, and dashboards.
• Establish trusted source-of-truth metrics for business and product including activations, retention, and feature usage.
• Build and maintain models, pipelines, and metric layers ensuring consistency across teams.
• Partner with product and engineering to improve instrumentation and telemetry coverage for new features.
• Make data self‑serve through clear dashboards, documented datasets, and reusable metric definitions.
• Improve reliability with data quality checks, freshness monitoring, lineage, alerting, and backfills.
• Collaborate on analysis behind recommendations, customer signals, and roadmap decisions.
• Keep the stack secure and cost-aware with access control and retention policies.

Skills

SQL

Data modeling

Data warehousing

Python

dbt

ETL pipelines

BI tools

Data quality

Data governance

Tools

Redshift

Snowflake

BigQuery

Postgres

Metabase

Looker

Mode

Cube

Job description

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.

What We’re Looking For

• 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.

Bonus

• 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.