Data Engineering
From limited cloud skills to full data autonomy, in record time
How a global luxury watchmaker went from limited cloud skills and manual workarounds to owning a modern, self-sufficient data platform built to last.

A global luxury watchmaker in the manufacturing sector, based in Switzerland, undergoing a multi-year data transformation.
Industry
Manufacturing (Luxury watches)
Location
Swiss
Stack
BigQuery · dbt · Looker · Airflow · Kubernetes · Terraform · GCP
Overview
Platform delivered and team upskilled simultaneously.
End-to-end pipelines: ingestion, transformation, and delivery.
Team now fully autonomous on a 12-tool cloud stack.
Background
The client
A global luxury watchmaker with limited internal cloud expertise, embarking on a multi-year data transformation. Their ambition went beyond migrating to GCP: they wanted the internal team to genuinely master modern data engineering, building skills in parallel with the platform, so they could own and evolve their data capabilities independently from day one.
The challenge
Deploy fast. Learn as you go. Own it forever.
The team lacked the internal cloud expertise and needed to deploy a production-ready architecture on GCP as quickly as possible, without sacrificing the knowledge transfer that would make them truly autonomous afterwards.
At the same time, users were stuck manually refreshing datasets, data was scattered across siloed sources, and there was no single source of truth. Every delay in solving this eroded trust in the data and created compounding organisational inefficiencies.
Solution
Full data autonomy
01
A production-ready platform, architected for longevity
Astrafy built an end-to-end platform on GCP, covering data ingestion from on-premises databases, transformation with dbt, and delivery of business-ready data marts in Looker.
Every architectural decision was documented and explained, while key practices such as CI/CD, Infrastructure as Code, and data governance were transferred to enable independent ownership.
02
Eliminating manual work to free the team to grow
Before the project, users manually intervened to refresh datasets — a constant drain on time and a source of data quality issues.
Astrafy automated the full pipeline using Airflow, Dataflow, and Pub/Sub, freeing analysts to focus on insight rather than maintenance. With SQL-accessible, always-fresh data in BigQuery, business teams gained self-service capabilities.
03
Building internal expertise alongside the platform
Astrafy's close involvement, availability, and knowledge-sharing approach meant the internal team progressively took ownership of every layer of the stack. By the end of the engagement, the client had the expertise and confidence to develop their data capabilities entirely on their own terms.
Results
Full ownership. Zero dependency.
Team fully autonomous on the cloud-native stack
Manual data refresh effort eliminated
Single source of truth accessible to all teams
Self-service analytics
Scalable, AI-ready platform ready
Coherent data governance framework in place
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