Enabling a data-driven organization

Accelerate data democratization in your organization

A modern data landscape

Building a Modern Data Landscape

Your organization understands the value of data, but struggles to put the right data in the hands of the right people at the right time.  A modern data landscape is essential in your organisation when you want get value out of all you data. To build a modern data landscape you need a modern data stack, including a data warehouse that allows you to build data sets that are: up to date, reliable, maintainable, documented, well-governed and ready for cloud. But also, intelligent tools and the right skills in your team are needed for a foundation on which you build an organization where data democratization is a reality.

Ending of the Traditional Data Warehouse

The traditional data warehouse is moving to the cloud and a new stack of tools, often driven by open source initiatives have changed the playing field beyond recognition. Until recently, data was “owned” by IT departments. Business units such as marketing, used the data to make business decisions, but they always had to go through the IT department to get the data. The goal of data democratization is to have anybody use data at any time to make decisions with no barriers to access or understanding. Data needs to be generally available and a modern data warehouse enables that data democratization. A radical paradigm shift has started in the Business Intelligence (BI) space and the end of the traditional data warehouse is near, ready to be replaced by more relevant architectures that can be deployed on the cloud within the blink of an eye.

The Modern Data Stack

A modern data stack is centered on a powerful data warehouse. Data is loaded directly into the warehouse. A robust and reliable transformation layer is used to turn that raw data into dependable and meaningful datasets. As a company we have partnered with many key players in the ecosystem and below we give suggestions for solutions to start using in your organization.

  • Cloud Data Platform: Azure,  AWS, GCP
  • Modern data warehouse: Amazon Redshift, Google BigQuery, Snowflake, DataBricks
  • Business Intelligence: Tableau, PowerBI. Looker
  • Transformation and Orchestration: Apache Airflow, DBT
  • Data Ingestion: Fivetran, Stitch

We can help you make the best selection possible based on the unique setup at your organization.

Allows you to build data sets

The Modern Data Team

As data tools change, so do the people who use them. Business users want to self-serve and be data-driven so the data has to be ‘ready’ to use. Recently, we introduced the role of Analytics Engineer to act as the bridge between data engineers and data analysts. It is their job to build well tested, up to date and documented datasets that the rest of the company can use to answer their own questions. They are technical enough to easily be able to apply software engineering best practices like version control and CI/CD but also need to be able to communicate effectively with stakeholders to teach them about these things.

Doing Analytics right, by getting the right data in the hands of the right people at the right time. Fully automated and Iteratively!



Data engineers

Manage the generic data infrastructure to make the raw data available and accessible across an organization.

Data analysts

Partner with business stakeholders to answer questions with data, build dashboards and reports, and to carry out exploratory analysis.

Data scientists

Use statistics and machine learning to build models for optimization, forecasting, or outlier detection to be used in products or services and continuously improving those through experimentation.

Machine learning engineers

Create smart, machine-learning driven products in production and understand what it takes go from the laptop of the data scientist to running 24/7 in a secure, robust environment.

Cloud engineer

Builds a foundation build from native cloud services in order to safely and securely deploy products on a cloud platform.

Why an Analytics Engineer

A bridge between data engineers and data analysts

The role of analytics engineer recently emerged because organizations now understand the value of data-driven decision making and you have trained domain experts to become analysts and hired data engineers to make the data readily available. However, there is often not enough synergy between the two roles.

Data Engineers

Are not domain experts but focus on building a generic infrastructure, so they: Have fewer points of contact with different departments (e.g. finance, sales, marketing). Are less attuned to the data needs of different stakeholders and departments. Have limited experience using the tools such as Excel, Tableau, Power BI, or Alteryx.

Analytics Engineer

Improve the accuracy, reliability, security and speed of delivery of analytics workflows. Speed up the organization-wide adoption use self-service analytics. Start a sane, agile data governance that focuses on value, quality and use of data instead of on process and roles.

Data Analysts

Have no or very limited programming skills. Are unsure how to benefit from version control, documentation, automation, or testing. Have limited experience with a modern data warehouse with tools such as DBT, Stitch or those provided by Azure, AWS or GCP.

Work as an Analytics Engineer?

Come join our growing team of Data Experts

More about this position
Check out this interview with our Analytics Engineer Juan Perafan and Stijn Tonk Chief Strategy, at our technology conference GoDataFest. They share their insights on the upcoming role of the Analytics Engineer in the modern data team and how it ads value to your organization.


  • GoDataDriven stack: Azure
  • GoDataDriven stack: Amazon Web Services
  • GoDataDriven stack: Google Cloud Platform
  • Databricks logo - GoDataDriven
Get in touch with the experts

Let's discuss the next step

Are you interested in how we can help your organization? Contact our Sales Manager Tim Waijers if you want to know more. He’ll be happy to help you!