Analytics engineering

How Analytics Engineering is strengthening modern day businesses

One of the biggest challenges software developers face today is gaining useful insights from the vast quantity of data available to them. Analytics Engineering is a relatively new technical discipline that combines elements of data engineering and analytics to enable faster, high-quality decision making. The field has applications in a broad range of complex problem spaces, from hyperlocal, real-time delivery optimisation to fraud detection. By providing higher quality data inputs to our models and algorithms, analytics engineering generates more relevant and insightful outputs that help us enhance the overall customer experience. 

Enabling faster, data-driven decisions

Analytics engineers are currently among the most sought-after big-data professionals in the industry, due to their ability to take raw, unstructured data and create cleaner, more structured data sets. This enables data science teams to work faster and allows other business analysts to make decisions with the aid of data, without needing a data specialist to unblock them.  

As analytics engineers collaborate with business analysts, data engineers, and data scientists, maintaining close relationships with each of these stakeholders is crucial. Data scientists and business analysts define the requirements of the data ‘products’ created by analytics engineers, while the data engineers set up the infrastructure and environment required to build those products, and help make them easily accessible to everyone who needs them. 

Skills Required for Analytics Engineers

While analytics engineers share many of the same skills as data scientists, analysts and data engineers, including a deep understanding of how data is used across industry, they also need to be able to effectively model and define quality standards for that data. This unique role, which sits at the intersection of data engineering and analytics, requires mastery of:

  1. Structured Query Language (SQL). This is the core skill of any analytics or data engineer, as it is the most common language used to interface with databases. A strong command of SQL is essential in order to query, debug and optimise data objects and pipelines.
  2. Data modelling. Being able to take raw, unstructured data and model complex interdependencies (in order to produce clean, reliable, intuitive data products that are useful to data scientists and business users).
  3. Data warehousing principles. An understanding of how modern cloud data warehouses store and process data is key to both of the above points, to enable greater efficiency for both developers and end users of the data.

Depending on how exactly the analytics engineering discipline is deployed within a particular organisation, there may be additional skill requirements, such as the use of specific coding languages and tools. Invariably, however, analytics engineers will be able to code extremely well and be confident using software engineering concepts like version control, testing automation and deployment.

Analytics Engineering in the food delivery industry

Analytics engineering plays an integral role in driving innovation in the food delivery space enabling data-driven decisions to provide the best possible experience, not only for consumers, but also for riders (delivery partners), restaurants, and grocery partners. 

Data is at the core of the business and is used to inform every single element of the food delivery experience, from pricing to menu structures, as new requirements emerge. By converting raw data collected from apps into user-friendly information, analytics engineering helps provide an environment that allows other teams to gain useful insights into consumer behaviour, ultimately facilitating more effective, high-quality decision making. For example, we recently created a new data product for restaurant partners, enabling them to view and interrogate key performance metrics for their business.  

Analytics Engineering is still a relatively young discipline and is expected to grow rapidly in the next few years as the volume of data being generated across the industry continues to increase. Analytics engineers are universal facilitators, making them a desirable hire for modern organisations, like ours, as we look to improve the quantity and quality of insights gleaned from available data.

The article has been written by Devesh Mishra, Chief Product and Technology Officer, Deliveroo

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