Data pipelines (ETL/ELT)
Automated, scheduled pipelines that move and transform data from every source you use.
Data & Infrastructure
Data engineering & analytics
Reliable data first. Everything else builds on it.
Before AI and before dashboards comes the plumbing: pipelines that pull data out of every tool you use, clean it and land it in one place your team can query.
Good AI starts with good data.
Most companies have plenty of data — spread across a CRM, spreadsheets, an ERP, web analytics and a production database that nobody wants to query. Data engineering brings it together: pipelines that collect and clean it automatically, a warehouse that stores it in one consistent shape, and dashboards that answer questions without a developer in the loop.
This is also the step most AI projects skip and regret. We build data foundations that are useful today for reporting and ready tomorrow for machine learning, with quality checks so numbers can be trusted and documentation so your team can maintain them.
Data Engineering & Analytics
What we offer
Data warehouses
A single, well-modelled store on BigQuery, Snowflake, Redshift or PostgreSQL.
BI dashboards
Dashboards in Power BI, Looker Studio, Metabase or custom web apps that show the numbers that matter.
Data quality & governance
Validation, monitoring and clear ownership so data errors are caught before they reach decisions.
Data preparation for ML
Feature pipelines, labelling workflows and training datasets built for machine learning.
Web scraping & data collection
Collecting public and partner data at scale, legally and reliably.
When it’s the right fit
- Reporting means exporting spreadsheets and combining them by hand
- Different teams quote different numbers for the same metric
- You want to start with AI but your data lives in many disconnected places
- Queries on your production database are slowing down your product
How we deliver
- 01Source inventoryA list of every system holding relevant data, who owns it and how it can be accessed.
- 02Data modelA clear structure for customers, orders, products — whatever your business revolves around — agreed with the people who use the numbers.
- 03Pipelines & checksScheduled loads with validation rules, so broken or missing data raises an alert instead of producing a wrong report.
- 04Dashboards & trainingThe first dashboards built on top, with documentation and a session so your team can extend them.
Technology
Tools we use for Data Engineering & Analytics
- Python
- SQL
- dbt
- Airflow
- BigQuery
- Snowflake
- PostgreSQL
- Power BI
Frequently asked questions
Do we need a data warehouse before starting with AI?
Not always, but you need reliable, accessible data. For many projects a focused pipeline for the data the model needs is enough to start; a warehouse becomes worth it as use grows.
Which data sources can you connect?
Databases, CRMs, ERPs, spreadsheets, web analytics, payment systems, APIs and files. If it has an export or an API, we can usually build a pipeline for it.
Can our team maintain the pipelines afterwards?
Yes. We use standard, well-documented tools, hand over the code and documentation, and can train your team.
Next step
Tell us what you want to build.
A free 30-minute consultation with an engineer — no obligation, reply within one business day.
Contact us