Most organisations already collect plenty of data: sales from a till or online shop, customers in a CRM, costs in accounting software, and a few important spreadsheets someone updates by hand. The problem is rarely a lack of data. It is that the data sits in different places, in different shapes, and nobody fully trusts the numbers.
An analytics engineer fixes that. The role sits between raw data and the people who make decisions, turning messy sources into clean, tested, documented data that reports, dashboards and data science can be built on.
What an analytics engineer actually does
- Brings data together from different systems into one place, usually a cloud warehouse such as Snowflake or BigQuery.
- Models it into facts and dimensions, so "revenue", "customer" and "order" mean the same thing everywhere.
- Tests it, so errors are caught before anyone makes a decision on bad numbers.
- Automates it, so data refreshes on a schedule without copying and pasting.
- Documents it, so the business is not dependent on one person's memory.
Analytics engineer, data analyst or data engineer?
| Role | Main focus | Typical output |
|---|---|---|
| Data engineer | Infrastructure and moving large volumes of data reliably | Ingestion pipelines, platforms |
| Analytics engineer | Transforming data into trustworthy, reusable models | Data models, tests, documentation, metrics |
| Data analyst | Answering business questions with data | Analyses, dashboards, recommendations |
In smaller organisations, one person often covers all three. That is where a freelance analytics engineer is most valuable: the structure and reliability of a data team, without hiring one.
The core skills
- SQL, including joins, window functions and CTEs. Start with the window functions guide.
- Data modelling, especially the star schema.
- A transformation framework such as dbt.
- A cloud warehouse such as Snowflake.
- Python for automation and the tasks SQL is not suited to.
- BI tools such as Power BI, and enough DAX to build reliable measures.
- Software habits: Git, code review, testing and CI.
- Communication: translating business questions into data definitions, and back again.
Signs your organisation needs one
- Reports take hours to prepare and are often late.
- Different teams quote different numbers for the same metric.
- Only one person knows how the key spreadsheet or query works.
- You have data in several tools but no single view of the business.
- Dashboards are built, then abandoned because nobody trusts them.
What a first project looks like
A typical engagement starts with a conversation about the decisions you need to make and the data you already have. Then comes a simple, documented model connecting your key sources, a set of tests, and automated reporting on top, often in Power BI, Excel or Google Sheets. The result is a set of numbers that update themselves and that everyone agrees on.
I work as a freelance analytics engineer from London and Italy, with organisations across the UK and Europe, and I am also open to analytics engineering roles. Get in touch for a free conversation.
Written by Alessandro Ecclesie Agazzi, freelance analytics engineer in London. Updated 30 September 2026.