Unlocking the power of Analytics Engineering

Analytics Engineering lies at the intersection of business, Data Engineering and Data Analytics. They understand the business requirements while also being able to deliver clean and transformed data ready for analysis. They are familiar with software principles, such as testing, version control and continuous integration. The data warehouse is their domain. They will keep it clean, organised and documented. Data consumers can rest easy knowing that the data quality is preserved while having the ideal structure for visualisations, reports or as input for their Data Science models.

The origins of Analytics Engineering

Since 2012 there have been major developments in the data tooling stack pushing the emergence of Analytics Engineering:

1. Cloud-based data warehouses made data storage and processing cheap and efficient which resulted in a shift from ETL (Extract, Transform, Load) to ELT (Extract, Load, Transform)
2. Data pipeline services simplified and standardized data extraction for a multitude of sources
3. Business Intelligence tools empowered data consumers to get the most out of their data

With these changes it became clear that data can now quite easily be collected and loaded in a data warehouse. Also, reports and visualizations are quickly built, speeding up the process of providing insights. Both these advancements highlighted the importance of data modelling and data quality.

Data teams, consisting of Data Engineers and Analysists, are responsible for these tasks. However, Data Engineers don’t have the domain knowledge to fully understand business needs and Data Analysists lack the technical knowledge to be able to perform the data transformations themselves.

Lastly, new tools were being developed focusing on the transformation layer. These tools provide either complete data platforms where data extraction meets data modelling (such as Fivetran or Stitch), or specific frameworks offering powerful toolsets for data modelling (like dbt).

As the demand for skilled individuals good at data transformations increased, along with the availability of suitable tools, the concept of Analytics Engineering emerged.

The responsibilities of an Analytics Engineer

As mentioned above, an Analytics Engineer requires skills in modelling data and preserving data quality. They also keep track of business needs and focus on gathering and combining information to meet the requested insights. Therefore, it is responsible for the following tasks:

1. Gathering business requirements
2. Aligning requirements with data
3. Cleaning data
4. Transforming data to meet requirements
5. Keeping data up to date
6. Testing data quality
7. Documenting data

An Analytics Engineer spends most of its time in the data warehouse, because, in the modern data stack, most, if not all, data transformations are performed within the data warehouse. The landing zone is their starting point and the final data models, which can be used for e.g., visualizations, are their product.

The position of an Analytics Engineer within a data team

Within the modern data team an Analytics Engineer works mostly with Data Engineers and Data Analysts. The Data Engineers build and use data pipelines to deliver raw input data to the data warehouse, while the Data Analysts use the final data models to build dashboards or provide deep insights to the business.

Even though, the responsibilities are often divided this way, there are no strict guidelines. An Analytics Engineer could, for example, also do the data extraction part or built the dashboard. Just as the Data Engineer or Analyst could model the data. However, the Analytics Engineer is seen as the specialist for data transformations.

Teams without Analytics Engineers are prone to struggle with slow iterations regarding modifications to existing data models or setting up new ones. Moreover, they lack a dedicated member who knows the ins and outs of the data models and the business logic behind it. Consequently, there is a gap in communications between the business and the data team, resulting in long waiting times for both sides.

The advantages of Analytics Engineering

In short, having an Analytics Engineer in your team, has the following advantages:

• Up to date, tested, documented and well-thought-out data models
• Faster iterations for additions and changes to the data structure
• Smoother communication and alignment with the business and its needs
• Clear division of responsibilities between extracting and loading versus transforming the data

Furthermore, by applying Software Engineering principles, teams can work more efficiently and effectively, improve code quality, minimize errors, and adopt a structured development approach. This results in a more scalable, maintainable, and reliable data infrastructure to ensure high data governance and good data lineage.

Do you want to enjoy the full benefits of having an Analytics Engineer in your team? As your data partner, we can introduce you to several Analytics Engineers who can strengthen your data team. Contact us to explore the possibilities.

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