Automation · Deep dive 06

Data Pipelines & ETL

Data in the right shape at the right time. Pipelines that move, transform, and land data where it's useful — warehouse, dashboard, or the next automation. Reliable, observable, versioned.

What this covers

Production data pipelines: ingestion from source systems, transformation (SQL-first or dbt), landing in a warehouse (Postgres, BigQuery, Snowflake, ClickHouse), with proper tests, scheduling, and backfills.

Does this sound familiar?

'Monday dashboards' requires someone to manually refresh three CSVs first.
Reports contradict each other because they pull from different sources.
The 'data warehouse' is a Google Sheet with 60,000 rows.
Nobody knows exactly when last night's ingest ran, or if it did.
Adding a new source requires a week of SQL surgery because there's no pattern.

The customer payoff

What you get

What you feel once it’s running.

A warehouse you trust — one answer per question.

Scheduled, observable pipelines with retries and alerts.
Transformations expressed in SQL that analysts can read and extend.
Backfills + schema migrations that don't break production.

Phases

⏱ 6–12 weeks typical

How Data Pipelines & ETL actually runs.

01
Inventory

List every source + destination, including the hidden ones. Map ownership and freshness requirements."
02
Design

Pick ingestion tool (Airbyte, Fivetran, custom), warehouse shape (Postgres / BigQuery / Snowflake), transformation layer (dbt)."
03
Build + test

Pipelines in Airflow / Dagster / Prefect, transformations in dbt with tests. Data quality tests fail builds."
04
Migrate + cut

Old feeds stay running in parallel for 30 days. Cut over only when dashboards match."

The hand-off

The package

What lands in your hands — every artefact, nothing hidden.

Warehouse with documented schema
Scheduled pipelines with alerts + retries
dbt project with tests + lineage
Runbook for common failures + backfills
Migration guide from old sources
BI tool connections (Metabase, Looker, or your choice)

Straight questions

Q·01 Fivetran or Airbyte or custom?

Fivetran if the sources are on their connector list and cost is acceptable. Airbyte when you want self-hosted or non-standard sources. Custom pipelines when neither fits."
Q·02 Which warehouse?

Postgres up to ~100GB analytical data and it stays cheap + simple. BigQuery for ad-hoc scale + Google Cloud shops. Snowflake for serious analytics workloads. ClickHouse when you need real-time and can tolerate the ops cost."
Q·03 Do you do real-time?

When warranted. Batch is almost always enough; we'll push back on real-time if it's for 'real-time' dashboards that nobody actually watches live."
Q·04 Can analysts maintain this?

dbt specifically chosen so they can. Transformations are SQL they can read + extend. Ingestion + orchestration stays engineering-owned."
Q·05 What about GDPR / data retention?

Reviewed per pipeline. PII masking, retention policies, and access roles built in from scope day one."

Ready to start

Data you can actually act on.

Two-day audit of sources + destinations, honest shape of the warehouse, clear build plan. Start with what your dashboards are lying about.

Start a pipeline engagement

The wider map

Every service page at a glance.

Each link below opens a dedicated page on that specific piece of one of our four service pillars. Jump sideways — different service, same way of working.

Data Pipelines & ETL

Does this sound familiar?

What you get

How Data Pipelines & ETL actually runs.

Inventory

Design

Build + test

Migrate + cut

The package

Straight questions

Data you can actually act on.

Every service page at a glance.

Digital Product Strategy

Web & Mobile Development

Business Automation

AI Integration