Hire your AI data engineer
Pipelines, warehouse models, and analytics-ready tables — run by chat.
Your AI Data Engineer builds the pipelines and warehouse models that turn scattered product, billing, and marketing events into tables your whole team can query. Ingests from your app DB, Stripe, GA4, PostHog, and 50 other sources; models in dbt; tests for freshness and quality; and keeps the cost flat. Data stops being a blocker.
What your AI Data Engineer does
Workflows on autopilot
Without vs With a AI Data Engineer
- —You join product, Stripe, and GA4 data in a Notion table every quarter
- —Data engineer hires cost $200K+ and take 4 months to hire
- —Nobody documents what 'active_user' means and the number drifts across teams
- —Warehouse bill jumps $3K/month because a dashboard runs a full scan hourly
- —A schema change breaks 8 dashboards and nobody notices for a week
- ✓Warehouse mart answers the question in 3 seconds, any day of the week
- ✓AI engineer is productive in week one at a fraction of the cost
- ✓Every mart column has a business definition that's the source of truth
- ✓Weekly cost audit catches waste before it compounds
- ✓Schema changes follow additive-first protocol with deprecation periods
A day in the life of your AI Data Engineer
Tools your AI Data Engineer uses
Frequently asked questions
Do I need a warehouse at $10K/month revenue?
Probably not. Below about $1M ARR, most founders get by with well-queried production databases and platform-specific analytics (PostHog, Stripe dashboards, GA4). The AI Data Engineer can help either way — for smaller companies it tightens the existing queries and builds lightweight mart tables in Postgres; for larger companies it moves you to a proper warehouse (BigQuery is usually cheapest to start). The rule of thumb: when analytical queries are slowing your app's production DB or when you need to join 3+ sources regularly, it's time.
Which warehouses does it support?
First class: BigQuery, Snowflake, Databricks, Redshift, Postgres (for smaller workloads). Transformation: dbt Core, dbt Cloud, SQLMesh. Ingestion: Fivetran, Airbyte (self-hosted or cloud), Stitch, Meltano, custom Python with DLT. Event streams: Segment, Rudderstack, PostHog, Snowplow. BI: Hex, Mode, Lightdash, Metabase, Omni. The specific recommendation depends on your team size and existing tools; the AI Data Engineer proposes a stack and explains tradeoffs rather than forcing one.
How does it handle PII and data privacy?
PII columns are tagged in the source model and automatically excluded from the downstream marts unless explicitly whitelisted. Email addresses get hashed for analytics joins; raw values stay in a restricted schema with IAM controls. The AI Data Engineer works with the AI Security Engineer on data classification policy and enforces it at the dbt test level — a mart that references an unhashed email column will fail the build. For regulated workloads (HIPAA, GDPR deletion requests), it builds the deletion pipeline and runs it on request.
What about data quality and broken pipelines?
Every mart table has dbt tests: freshness (when was the last row inserted), uniqueness (are primary keys unique), referential integrity (do foreign keys resolve), and business rules (e.g., revenue can't be negative). Test failures create an incident with a row-level diagnostic. Common failure modes — a source API returning null fields, a schema change upstream, a time zone shift — have runbooks so the AI Data Engineer can triage in minutes instead of chasing edge cases. Most weeks have 1-2 test failures and all resolve within an hour.
Can it replace a full analytics engineer?
For most companies under 20 people, yes. What it doesn't replace: the strategic conversation about what metrics matter and how the business measures success — that's still a human-driven conversation with your AI Operations Analyst, your founders, and occasionally a fractional head of analytics. What it does replace: writing dbt models, fixing broken pipelines, onboarding new sources, maintaining the catalog, running cost audits. Above 20 people, many founders keep the AI Data Engineer and hire a human analytics lead whose time goes entirely to strategy and cross-functional work.
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