Why you need Data Engineers
Data Engineers are the IT specialists who are creating data pipelines between the services. This role is determined by a demand for stable integrations between services.
Phases of collaboration with Data Engineers
Firstly, let’s figure out the MVP for the initial launch. On this stage, cornerstones of the integration are to be defined. Based on this, we could make subsequent steps and add extra elements.
In terms of data strategy, the best way to understand the data flow is to visualize it. Picturing schema doesn’t merely show us data flow, but also the sequence and combination of the elements.
On the grounds of data engineers’ experience, the aggregation place is supposed to be chosen. One of the most popular options is Cloud Data Storage. The most obvious advantages of cloud solutions are credibility, safety and simplicity when it comes to scalability. For instance, the share of Amazon Web Services is 32%. (link) The alternatives are Microsoft Azure and Google Cloud Platform.
The first bullet point on the to-do list is finding an engineer capable of coding. However, the earlier the engineer gets involved in the designing process, the better instruments might be applied.
There exist 2 types of data engineers:
- Gentle data engineers, which are from Business Intelligence environment;
- Hardcore data engineers, who come from engineering.
Gentle data engineers build their solutions based on ready-made connectors and don’t necessarily need deep experience in coding to begin with. Hardcore data engineers can build the systems from the scratch and write missing parts of the data pipeline.
Gentle data engineers are historically close to the business therefore they understand business processes better.
For a Google Spreadsheet driven company, hiring this kind of person can decrease the impact of human factors and increase the accuracy of reporting. For the organisation as a whole, there is a growth opportunity towards more creative work and more stable processes.
Prioritisation and specialisation made possible. Companies can focus on their core competencies and use the ready-made solutions instead of writing connectors to the services. The most popular services for data extraction are Fivetran and Stitch.
Personally, the position of Data Engineer is my new personal favourite. Professionals performing this role create structures suitable for analysis and provide solutions impossible to come up with while staying focused on the product development. This role of Data Engineer emerged as a response to the need of constantly providing new answers for the question “How?”. The above is thanks to understanding of how to build pipelines and data flows which requires staying up-to-date with all technologies. Alongside this, deep expertise in terms of business processes is also essential. There is no challenge in merely closing the position, it is more about classifying the challenges and matching the right person to lead the way.