Find dbt Developer Leads on GitHub

The dbt Ecosystem Is a High-Value Developer Market

dbt (data build tool) has become the standard transformation layer for modern data stacks. dbt Core has over 9,000 GitHub stars and an enormous ecosystem: packages (dbt-utils, dbt-expectations, dbt-audit-helper), adapters (Snowflake, BigQuery, Redshift, DuckDB, ClickHouse), testing frameworks (Elementary, Great Expectations, Soda), and orchestration integrations (Airflow, Prefect, Dagster). Developers actively working in this ecosystem — committing dbt models, opening issues about MetricFlow metrics, or starring dbt-core — are prime prospects for data observability, data catalog, orchestration, and analytics tooling companies.

dbt GitHub Signal Sources

Stargazer Signals

• Stars on dbt-labs/dbt-core — core dbt adopters and evaluators
• Stars on dbt-labs/metricflow — semantic layer and metric definition adopters
• Stars on dbt-labs/dbt-utils — active dbt macro users
• Stars on dbt-labs/dbt-expectations — dbt testing ecosystem adopters
• Stars on elementary-data/elementary — dbt data observability evaluators
• Stars on dbt-labs/dbt-semantic-layer — semantic layer integrations
• Stars on daxtens/dbt-audit-helper — data migration and audit users

Keyword Signals in Issues & Discussions

• "dbt model" or "dbt source" — active dbt users defining data models
• "dbt semantic layer" or "metricflow metric" — MetricFlow adoption signals
• "dbt test" or "dbt-expectations" — dbt testing and quality signals
• "dbt package" or "packages.yml" — package ecosystem users
• "dbt Cloud" or "dbt job run" — dbt Cloud evaluation or adoption
• "elementary" or "dbt data observability" — monitoring ecosystem adopters
• "dbt incremental" or "dbt snapshot" — advanced dbt pattern work

Who Buys dbt Developer Leads

• Data observability platforms: Monte Carlo, Acceldata, Lightup, Metaplane
• Data catalog tools: Atlan, DataHub, Alation, Stemma (Collibra)
• dbt-adjacent testing: Great Expectations, Soda Core, re_data
• Analytics BI tools targeting dbt: Lightdash, Superset, Metabase, Evidence
• Data orchestration: Dagster, Prefect, Airflow integrations with dbt support
• Data warehouse vendors: Snowflake, BigQuery, Databricks, DuckDB, MotherDuck, ClickHouse
• dbt Cloud competitors: custom dbt runners, open-source orchestration tools

Interpreting dbt Lead Signals

A new star on dbt-labs/dbt-core from a "Senior Analytics Engineer at Airbnb" is very different from one from a "data analyst at a 10-person startup". GitLeads returns the full profile — company, bio, location, follower count, top languages (Python and SQL are most common for dbt users) — so your SDRs can personalize at scale. Keyword signals give even more context: a developer opening a GitHub Issue about "dbt incremental strategy merging wrong in Redshift" tells you exactly what warehouse they're using and what pain they're experiencing.

// GitLeads keyword signal for dbt users
// Configure in dashboard: keyword = "dbt semantic layer"
// Sources: GitHub Issues, PRs, Discussions, commit messages

{
  "signal_type": "keyword",
  "keyword": "dbt semantic layer",
  "context": "PR: 'Add MetricFlow measure definitions to semantic layer'",
  "lead": {
    "github_username": "analytics_eng",
    "name": "Jordan Park",
    "email": "jordan@datacompany.io",
    "company": "DataCo Analytics",
    "bio": "Analytics Engineer | dbt | Snowflake | MetricFlow",
    "top_languages": ["Python", "SQL"],
    "followers": 180,
    "location": "New York, NY"
  }
}

Pushing dbt Leads to Your Sales Stack

GitLeads integrates with HubSpot, Salesforce, Slack, Smartlead, Instantly, Apollo, Clay, and 10+ other tools. For data tooling companies, a Slack alert to a "#dbt-leads" channel works extremely well — your sales or DevRel team sees the signal and context in real time and can craft a highly personalized response. The lead arrives with languages, company, bio, and the exact GitHub signal — enough to write a genuinely useful first message.