Full Stack Analytics & Data Pipelines warehouse, ETL, transformation, BI — all wired together
A full-stack analytics build is what every growing company needs by year three — when one-off connectors and ad-hoc dashboards stop scaling. The modern data stack has settled around five layers: warehouse, ELT (load), transformation (dbt), BI (visualisation), and reverse-ETL (push back to ops tools). We build all five for you, configured for your scale and budget, and hand it over with documentation so your team can run it.
Stack defaults: BigQuery or Snowflake for warehouse (Postgres / DuckDB if data is small), Fivetran / Airbyte / Stitch for ELT, dbt for transformation, Looker Studio / Metabase / Tableau / Power BI for BI, and Hightouch / Census for reverse-ETL when ops tools need the cleaned data back. Output: a single source of truth, dashboards that match revenue, and a stack your data team can extend without us.
Common problems we fix
- Six tools, six versions of "revenue" — nobody knows which is right
- Engineers writing connectors and ETL scripts that break weekly
- Marketing dashboards drifting from finance dashboards
- No transformation layer — every dashboard rewrites the same logic
- Cannot push cleaned data back to CRM / ad platforms / email
What you get
- Warehouse selection + setup (BigQuery / Snowflake / Postgres)
- ELT pipeline (Fivetran / Airbyte / Stitch / Singer) for sources you specify
- dbt project: staging, marts, semantic layer, tests, docs
- BI tool selection + dashboards on top (Metabase / Looker Studio / Tableau)
- Reverse-ETL setup (Hightouch / Census) when ops tools need the data back
- Data dictionary, lineage docs, and SLA on each table
- Cost-monitoring + query-optimization for warehouse spend
- CI / CD for dbt with PR-based review of model changes
- Hand-off + training for your in-house data team
Want a plan tailored to your situation? Let's talk specifics.
Free 20-minute call. We will review your current setup, flag what is broken, and share what we would do first. No slides, no pitch deck.
Book ConsultationQuestions about full stack analytics
BigQuery if you live in the Google ecosystem and value cheap storage + per-query pricing. Snowflake if you have multi-cloud needs and want compute-storage separation. Both are fine — we pick on discovery.
Fivetran when you have budget and want zero ops. Airbyte (open-source self-hosted) when cost matters and you have engineering bandwidth. We have shipped both.
dbt gives you version control, tests, documentation, and lineage on your transforms. After 50 SQL files, raw SQL becomes unmaintainable. dbt is the industry standard for a reason.
8–12 weeks from kickoff to first working analytics layer. We deliver in milestones — warehouse + first source live in week 4, full stack in week 12.
Yes. 4–6 hours of structured training + Loom recordings + written playbooks covering every layer. Optional ongoing retainer if you want us to keep extending.
Let's talk about your business.
Tell us what you're working on and where you want to go. We'll put together a plan. No obligation, no sales pitch.
- Free 30-minute call
- A plan built around your goals
- No obligation, no pressure
- Your own account manager