Understanding and interpreting data is the final stage of a long journey. Data starts in its raw format is then compiled, cleaned and translated into information, knowledge and finally wisdom.

One of the most common challenges companies face when investing in improving their data process is bridging the gap between data infrastructure and analytics. The answer to this problem is to hire a data engineer.

A data engineer is an engineering role within a data science team or any data related project that requires creating and managing technological infrastructure of a data platform.

Processing data systematically requires a dedicated ecosystem known as a data pipeline: a set of technologies that form a specific environment where data is obtained, stored, processed, and queried. So, along with data scientists who create algorithms, there are data engineers, the architects of data platforms.

Many organizations start with a mix of stop-gap data access solutions and data models. A comprehensive, automated data solution — a modern data stack — allows you to systematize the whole process, allowing you to build reusable, replicable data models, dashboards, and reports based on known needs of your business.

The modern data stack is capable of establishing a strong technical infrastructure, but a lot of data infrastructure is inappropriate to a company’s data processing needs. Hiring qualified data engineers can prevent this issue.

Great solutions will free up data engineers to work on projects that actually move the needle for their companies, and let data analysts explore the data for themselves. To achieve this, your data team should carefully design a data stack and conduct infrastructure analysis to assess what makes the most sense for your organization.

There are three main functions a data infrastructure.

  1. Extracting data: The information is located somewhere, so first we have to extract it. In terms of corporate data, the source can be some database, a website’s user interactions, an internal ERP/CRM system, etc. Or the data may come from public sources available online.
  2. Data storing/transition: The main architectural point in any data pipeline is storage. Organizations need to store extracted data somewhere. In data engineering, the concept of a data warehouse embodies an ultimate storage for all data gathered for analytical purposes.
  3. Transformation: Raw data may not make much sense to the end users because it’s hard to analyze in such form. Transformations aim at cleaning, structuring, and formatting the data sets to make data consumable for processing or analysis. In this form, it can finally be taken for further processing or queried from the reporting layer.

The responsibilities of a data engineer can correspond to the whole system at once or each of its parts individually.

General-role. A data engineer found on a small team of data professionals would be responsible for every step of data flow. So, starting from configuring data sources to integrating analytical tools — all these systems would be architected, built, and managed by a general-role data engineer.

Warehouse-centric. Historically, the data engineer had a role responsible for using SQL databases to construct data storages. This is still true today, but warehouses themselves became much more diverse. So, there may be multiple data engineers, and some of them may solely focus on architecting a warehouse. The warehouse-centric data engineers may also cover different types of storages (NoSQL, SQL), tools to work with big data (Hadoop, Kafka), and integration tools to connect sources or other databases.

Pipeline-centric data engineers would take care of data integration tools that connect sources to a data warehouse. These tools can either just load information from one place to another or carry more specific tasks. For example, they may include data staging areas, where data arrives prior to transformation. Managing this layer of the ecosystem would be the focus of a pipeline-centric data engineer.

Regardless of the focus on a specific part of a system, data engineers have similar responsibilities. This is mostly a technical position that combines knowledge and skills of computer science, engineering, and databases. Data engineers should do their best to bridge gaps between data and the business, bring data consumers into their process, and build community around data.

Just remember that hiring one data engineer at your company won’t solve all your business problems — though it will help — but building a data-driven culture will.

Data-driven business analyst focused on gathering vital business intelligence to meet company needs and passionate about showing how easy analytics can be