Case study

Deel: Rebuilding the Data Function at 90 → 2,000+ Employees

I joined Deel as its second data hire — a growth data analyst, brought in directly by the Head of Growth — while the company scaled from 90 to 2,000+ employees. Analytics ran on a Postgres database and Metabase, every chart backed by its own hundreds of lines of hand-written SQL with no modelling layer underneath: a setup that worked at small scale and had stopped scaling with the company.

The transformation · Postgres to a source of truth

1. 01

   Making the case

   I took the scaling problem to leadership and made the case to hire a dedicated head of data.

   Decision — The constraint was organisational before it was technical — chart-level SQL scales with analyst hours, not with headcount.

2. 02

   Stakeholder discovery

   I interviewed product, growth, and sales to define what each function actually needed from data.

   Decision — The existing charts recorded what had once been asked for; requirements had to come from the business, not from what Metabase already showed.

3. 03

   Re-platforming

   SnowflakeFivetran

   With the new data leader on board, we moved warehousing onto Snowflake and put ingestion on Fivetran.

   Decision — Analytics needed its own warehouse and managed ingestion, so the effort could go into modelling instead of moving data.

4. 04

   Modelling the source of truth

   dbtSnowflake

   I dimensionally modelled the warehouse in dbt — version-controlled, tested, one definition per metric.

   Decision — Every number defined once in the model, instead of once per chart in hand-maintained SQL.

5. 05

   Self-service BI

   Looker

   We rebuilt reporting in Looker on top of the modelled layer; teams answered their own questions, and ad-hoc analysis requests fell by roughly 60%.

   Decision — Once definitions live in the model, BI becomes an access layer — the same numbers for everyone, without an analyst in the loop.

What shipped

• Snowflake warehouse with Fivetran ingestion, replacing analytics run directly against Postgres
• A dimensionally modelled dbt project: tested, version-controlled, a single definition per metric
• Looker self-service BI built on the model, replacing per-chart SQL in Metabase
• Documented data requirements per function — product, growth, sales — from direct stakeholder discovery
• A data function under dedicated leadership: a hire I made the case for, an org design that scaled with the company

Stack

Dashboards get rebuilt; the modelled foundation under them was built once.