Node DATA.01

Data engineering and modern data platforms

Pipelines and a warehouse your team stops arguing about.

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Data pipelines, warehouses and lakehouses, transformation and orchestration on Snowflake, BigQuery and PostgreSQL, built so reports and AI agree.

The problem

Data sits in an ERP, a CRM, payment gateways, spreadsheets and SaaS tools. Every report is a manual export, and finance, sales and operations each have a different number for the same thing.

A modern data platform brings that data into one place on a schedule, models it into tables people understand, and tests it so errors are caught before they reach a dashboard.

What we build

Ingestion pipelines from databases, SaaS APIs, files and event streams
Cloud data warehouses and lakehouses on Snowflake, BigQuery or PostgreSQL
Transformation models with dbt: tested, documented, version-controlled
Orchestration and scheduling with Airflow or managed equivalents
Data quality tests, alerting and lineage

How it works

Sources are extracted on a schedule into a raw layer, transformed into clean, modelled tables, tested, and served to dashboards, applications and AI systems.

Step 1Audit sourcesList every source, its owner, refresh needs and quality issues.
Step 2Design the modelAgree core entities and metric definitions with the business before writing code.
Step 3Build pipelinesExtraction, loading and dbt transformations, all in version control.
Step 4Test and documentTests on keys, freshness and business rules; documentation generated from the code.
Step 5Hand overRunbooks and training so your team can extend it.

Deliverables

Data platform deployed in your cloud account
Pipelines for the agreed sources
Modelled tables with metric definitions
Tests, alerting and documentation
Runbook and training

Typical stack

Python
SQL
Snowflake
Google Cloud

We choose tools to fit your environment; this is where we usually start.

Ways to work with us

Build and HandoverA defined system, delivered, documented and handed to your team.Engagement Forward Deployed EngineersOur engineers inside your team until it runs in production.Engagement Managed AI OpsOngoing monitoring, evaluation and improvement of your AI systems.Engagement

Questions

Snowflake, BigQuery or PostgreSQL?

It depends on data volume, your existing cloud, team skills and budget. Smaller workloads often run well on PostgreSQL; we recommend the simplest option that will hold up for the next few years.

Do we need a data team to maintain it?

Someone needs to own it. We design for a small team, document everything, and can provide ongoing support if you do not have that person yet.

How long until the first useful report?

We sequence the work so a first set of trusted tables and a report on them arrive early, then widen coverage source by source.

Adjacent nodes

Part of Data.

AI-ready data foundationsClean, modelled, governed data, so AI systems give answers you can use.DATA.02 Executive and analytics dashboardsDashboards people open every morning, because the numbers are right.DATA.03 Database solutions and cloud migrationFaster queries, safer data, and a cloud move without the weekend outage.DATA.05

Talk to us about data engineering and modern data platforms for your business.

Send a short brief. We reply by email, usually with questions before we propose anything.