Map
We inventory your sources, define every metric once, and write a one-page spec you sign off before anything is built.
5+
Years building production data systems
20+
Hours saved per week, per client, on reporting
15+
Happy teams running on our backends
40+
Production dashboards shipped to clients
— 01Sound familiar?
If any of this rings a bell, we should talk.
Most clients hit at least three.
- Meta says one thing, Shopify says another, and nobody knows which is right.
- Someone spends two days a month pulling numbers into a deck.
- Reporting is held together by Supermetrics, Sheets and hope.
- Every team (or every client) has its own version of the numbers.
- Adding a new client, brand or channel means rebuilding reports by hand.
DATA CHAOS
DATA CLARITY
— 02How we work
The same four steps,
every time.
Not a menu of services — a sequence. You always know what happens next.
Build
Pipelines into a warehouse you own — BigQuery, Snowflake or Postgres. dbt models make spend, revenue and conversions match across platforms. Custom connectors where none exist.
Report
Dashboards across every channel, client and brand, refreshed automatically before your team starts. Every metric defined in a shared dictionary.
Run
Monitoring, API changes, new sources and clients, new dashboards — a monthly retainer that works like your in-house data team, minus the hiring.
Our expertise
Four skills. One backend.
Everything we build sits on the same four capabilities. Here's what each one means for you in practice.
API integration
PythonGitHub Actionsn8nMake
When a tool has no off-the-shelf connector, we write one — and we keep it working when the API changes.
Custom connectors
Niche ad networks, affiliate platforms, call tracking, internal tools, that one CRM nobody supports — pulled into the warehouse on a schedule.
Two-way syncs
Warehouse to CRM and back: enriched records, statuses and revenue flowing both directions without manual exports.
Data pushed back to your tools
Offline conversions sent to Google and Meta, modeled metrics written into HubSpot, Sheets or Slack — where your team already works.
Maintained on retainer
API versions, rate limits, auth changes — handled as part of the monthly retainer, not as a surprise invoice.
Data engineering
BigQuerydbtAirbytePython
The warehouse and pipelines underneath everything else — built in your cloud account, owned by you.
Warehouse setup
BigQuery, Snowflake or Postgres in your own account, structured and documented so anyone you hire later can find their way around.
Pipelines that keep running
Airbyte or Fivetran where a connector exists, custom Python where it doesn't. Scheduled, monitored, with alerts in Slack when a feed breaks.
Modeling with dbt
One shared metric dictionary. Spend, revenue and conversions made to match across Meta, Google, Shopify and your CRM — tested and versioned in your repo.
Data quality & monitoring
Freshness and volume tests on every table, so a broken feed is caught before your team opens the dashboard, not after a client asks.
Dashboard development
Looker StudioMetabasePower BI
Reports your team actually opens — refreshed automatically, loading in seconds, built on modeled data rather than raw connector dumps.
Cross-channel marketing dashboards
Paid, organic, email and store revenue in one view, sliced per client, brand or channel, with the same definitions everywhere.
Client reporting for agencies
One template, every client, refreshed automatically. No more two days a month building decks from screenshots.
Executive & finance views
MRR, LTV, CAC and margin per client or brand, role-scoped: one view for the owner, one for account managers.
Built for speed
Dashboards load from pre-modeled tables, not live API calls — under two seconds instead of thirty, and no Supermetrics bill.
AI automation
ClaudeOpenAIn8nPython
Agents and automations on top of your real numbers — built once the data layer is live, so they never guess.
Automated reporting
Weekly performance summaries written from the warehouse and delivered to Slack or email, per client or brand, before Monday's standup.
Agents that answer questions
"What was CAC by channel last week?" — answered from your data with the query shown, not a hallucinated number.
Lead scoring & routing
Scores built on your CRM and ad data, written back to the CRM so sales works the right leads first.
Anomaly alerts
Spend spikes, tracking breaks and ROAS drops flagged automatically, with the likely cause attached.
Order matters. We build the AI layer after the data layer — an agent is only as good as the numbers it reads.
— 03Why us
Why not just buy Supermetrics or Funnel?
You could. They're good connectors. Here's what's different about hiring the team instead of buying the tool.
| Capability |  Supermetrics |  Funnel.io |  Dublo |
|---|---|---|---|
| What it is | A connector: moves marketing data into Sheets, Looker Studio or a warehouse. | A marketing data hub: connectors plus light transformation in their UI. | A done-for-you data team: we build and run the whole backend. |
| Who does the work | You, or an analyst you hire. | You, inside their interface. | We do. You read the numbers. |
| Where your data lives | Wherever you send it. Setup and structure are on you. | Inside Funnel, with exports to a warehouse available. | Your warehouse — BigQuery, Snowflake or Postgres. Fully yours. |
| Metric logic (spend, revenue & conversions matching across platforms) | Do it yourself in Sheets or Looker Studio. | Field mapping and custom metrics in their UI. | dbt models we write, versioned in your repo, one shared metric dictionary. |
| Sources | Their marketing connector catalog. | Their connector catalog, marketing-focused. | Anything: ad platforms, stores, CRMs, internal tools, custom APIs. |
| Dashboards | Templates. You build and maintain them. | Build in their explorer or export to your BI tool. | Built for you, maintained for you. |
| When an API changes or something breaks | Connector gets fixed; everything downstream is your problem. | Connector gets fixed; everything downstream is your problem. | We fix it. Monitoring and maintenance are the retainer. |
| Pricing model | Per data source and user, monthly. | Scales with sources and ad spend. | Fixed-scope build, then a flat monthly retainer. |
| If you leave | The data stops flowing. | The data stops flowing. | Everything stays: warehouse, models, dashboards, documentation. |
| AI on top of your data | — | — | Agents and automations reading from your warehouse. |
Tool-agnostic: when a Supermetrics or Funnel connector is the right part for the job, we use it inside your build. Tool details as of this writing — check their current plans.
— 04The stack
The tools we use.
BigQuery
Snowflake
Postgres
dbt
Airbyte
Fivetran
Looker Studio
Metabase
Power BI
Dagster
n8n
Make
Zapier
Python
Claude
OpenAI
Slack
GitHub Actions
Tool-agnostic. We recommend what fits, not what we resell.
— 05In their words
What operators say
after the numbers finally line up.
01 / 04
Olifant Digital5 platforms unified — 20+ hours of manual reporting removed every week.Read the case study Ignite PPCCross-platform CPL and ROAS discrepancies eliminated across 125+ client accounts.Read the case study Get Hyped MediaDashboards from 30+ seconds to under 2 — and the Supermetrics bill gone.Read the case study
+ 11 other teams running on Dublo.
Read the case studies— 06Who you'll work with
Dorian Teffo
Founder · Data engineer
Hi, I'm Dorian. I build the boring,
reliable plumbing.
Four-plus years building analytics for marketing agencies, e-commerce brands and SaaS teams. I run Dublo because most "data teams" sell dashboards and walk away from the pipeline that feeds them. I do the opposite — I own the boring part, so the dashboards stay true.
Every engagement is scoped upfront, priced fixed, and built in your environment — your BigQuery, your Looker workspace, your dbt repo. When we're done, you own everything. If you ever bring it in-house, my final month is documenting it for your new hire.