Our services at a glance

We ensure a stable data flow

  • Data pipelines & ingestion: Development of automated interfaces for extracting and loading data from marketing, sales, finance and operational systems.

  • Cloud data warehouse integration: Connection of central target systems such as Google BigQuery or Microsoft Fabric as a consolidated database for analytics, BI and AI.

  • ELT/ETL development: Preparation and structuring of raw data for further processing – as a basis for data modelling, reporting and advanced analytics.

  • API & system integration: Connection of SaaS tools, databases and third-party systems via REST or GraphQL APIs, as well as secure connections to on-premise systems.

  • Migration & historisation: Secure transfer of historical data during system changes or architecture modernisations – including validation and reconciliation.

When is professional data engineering worthwhile?

The need often arises when simple solutions no longer scale:

  • High manual effort: Time is spent on exports, preparation and troubleshooting instead of analysis

  • Delayed reporting: Dashboards are not up to date because data is available too late or is unreliable.

  • Lack of overall view: Data from marketing, sales and operations cannot be linked consistently.

  • Dependence on individuals: Complex scripts or macros are not documented or maintainable.

How we work

We follow modern software engineering principles.

ELT-first, cloud-native
We collect data where it originates – via APIs, files or databases – and load it into the cloud data warehouse in a structured manner.

The actual transformation takes place centrally on the platform. This preserves raw data, makes logic traceable and allows for retroactive adjustments.

 

Automation & stability
We rely on robust, cloud-native orchestration to provide data reliably and predictably.

The goal is a stable data flow – independent of individual persons or manual intervention.

 

Operation, monitoring & reliability
Data pipelines are monitored. Errors, delays or failures are detected at an early stage.

Together, we define responsibilities and response times so that problems are resolved before they become visible in reporting.

What do you end up with?

  • Ongoing data pipelines: Automated jobs that reliably update your data.

  • Central database: A cloud data warehouse that brings together all relevant data sources.

  • Transparency of data structures: Documentation of available tables, fields and data sources.

  • Monitoring and stability: early warning systems for interface problems and loading errors.

Frequently asked questions (FAQ)

Where is my data stored? In the cloud environment you have selected. We take data residency requirements (e.g. Switzerland or EU) into account.

ELT or ETL – which makes more sense? For modern cloud platforms, we primarily rely on ELT, as it is more flexible and scalable. ETL is used where it makes technical or business sense.

Can on-premise systems also be connected? Yes. Local ERP or specialist systems can also be reliably integrated via secure connections.