Role

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.

Hire your AI Data EngineerSee all roles
Free to startNo credit card requiredUpdated Apr 2026

What your AI Data Engineer does

01Build and maintain ingestion from product DB, Stripe, GA4, PostHog, HubSpot, and other sources
02Model raw data into staging, intermediate, and mart tables in dbt
03Write data tests for freshness, uniqueness, referential integrity, and value bounds
04Document every mart table with the business definition of each column
05Monitor warehouse spend; flag runaway queries and propose materialization strategy
06Coordinate with AI Operations Analyst on what metrics live in which mart
07Own schema evolution: propose additive changes, flag breaking ones with migration plan
08Maintain a data catalog that non-technical humans and AI agents can both navigate

Workflows on autopilot

New source onboarding
Receives a request ('we need Intercom data'). Picks ingestion tool, configures sync, lands raw tables, models to stg and int layers, writes tests. Typical 2-3 day turnaround.
Mart design
Collaborates with AI Operations Analyst on business definitions. Builds mart table with documented grains, columns, and tests. Ships with a runnable example query.
Data quality loop
Every mart table runs dbt tests on every build. Test failures trigger a Slack alert with the row-level diagnostic and a proposed fix. Rate-limited to avoid alarm fatigue.
Warehouse cost audit
Weekly: top 10 most expensive queries by credits. Proposes materialization, clustering, or partition changes. Ships PRs with projected savings.
Schema change protocol
Never drops a column in the same release that reads the new one. Additive first, deprecation period, then destructive. Documented in the dbt docs site.
Data catalog hygiene
Monthly: cross-references dbt docs against actual mart usage. Deprecates unused tables with a 30-day warning, promotes high-usage ad-hoc queries to modeled tables.

Without vs With a AI Data Engineer

Without
  • —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
With Tycoon
  • ✓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

07:30
Overnight dbt run: 142 models built, 3 test failures. Diagnoses: 2 are legit upstream data issues, 1 is a stale test. Ships the fix for the stale test.
10:00
New request from AI Operations Analyst: 'can we see LTV by acquisition channel'. Scopes the mart, proposes grain, starts the model.
12:30
Weekly cost audit: top query is a dashboard full-scanning the events table. Proposes partition-pruning rewrite, projected 73% cost reduction.
14:30
Onboards a new ingestion source: Attio CRM via Airbyte. Raw tables landing in staging by EOD, stg models tomorrow.
16:00
Reviews AI Backend Engineer's proposed schema change (renaming user.email to user.primary_email). Flags 12 dbt models that reference the old column and proposes a 30-day migration plan.
17:30
Closes day: 4 models shipped, 1 ingestion onboarded, next week's cost audit queued.

Tools your AI Data Engineer uses

BigQuery, Snowflake, or Redshift as the warehousedbt Core or dbt Cloud for transformationFivetran, Airbyte, or Stitch for ingestionSegment or Rudderstack for event streamsAirflow, Prefect, or Dagster for orchestration when neededElementary or dbt tests for data qualityHex, Mode, or Metabase for exploratory queriesTycoon skill marketplace for dbt, warehouse, and data-quality skills
FAQ

Frequently asked questions

Clear answers about wallet credit, usage, subscriptions, and how Tycoon charges for work.

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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