In the current job market there are more open positions than data engineers. In the competition for this scarce talent, metallurgical companies are at a disadvantage because they are not readily associated with digital innovation. Nevertheless, the massive amount of value that digitization can unlock presents an amazing opportunity for motivated data professionals to create an impact and advance their career. It becomes a matter of communicating this potential to them. The first step in this process is writing a clear and informative job description.

Having switched jobs twice in my early career and having contracted through recruiters for nearly two years, I have seen a large number of job descriptions. Data engineering is a relatively new specialization, so there is still no clear consensus on what the major responsibilities are. Therefore, job descriptions vary wildly, often containing (too) long lists of “required” technologies and irrelevant information about the company. Most of all, they fail to paint a relatable picture of the person the company is looking for and how this person will fit into the existing team and data infrastructure.

Below I describe four strong points that great data engineer job descriptions for roles requiring less than 5 years of experience have in common, according to my experience and opinion.

1. Precise technological requirements

What technical skills does the data engineer really need? While some skills like Python and SQL are fundamental, most modern tools like Airflow and DBT are merely higher-level abstractions of these skills and can thus be learned on the job. Sometimes the required technology has not been decided yet. In that case, it’s fine to list technical requirements in general terms, i.e. “we need you to implement an orchestrator for our data pipelines”. Be wary of putting too many technologies in the job description. Requiring skills that are commonly attributed to data scientists in a data engineering job description creates the impression that the company doesn’t know what it’s doing.

2. Clear picture of the existing data infrastructure

If you’re a metallurgical company, your data landscape is unlikely to be a green field. This means that data is already stored in data historians and/or legacy systems, i.e. a brownfield. Is your company using OSIsoft PI, Aspen or SQL Server? A helicopter view of the existing data infrastructure allows the data engineer to gauge whether this role matches his or her current skill set and learning objectives.

3. Clear picture of the existing team

The data engineers’ main responsibility is providing the data scientist with access to clean and reliable data. This job can’t be done in isolation. It requires collaboration with data scientists using the data as well as the business team providing the data. Often, a data engineer is overseen by a data architect or manager. Variations in this structure result in a very different job focus for the data engineer.

When evaluation a position, a data engineer tries to gauge what his team will look like. Is there another data engineer that can act as a sparring partner? Is there a data architect or will I be responsible for designing the infrastructure? Is there a data analyst on the team supporting the data scientists or will this be my responsibility? Without an idea about the team structure, the data engineer can’t infer what his day-to-day work will look like.

4. Describe how the data engineer can add value

This part is, in my opinion, the most critical one. Data engineers with less than 5 years of experience are still developing their technical expertise. Some engineers prefer to focus on writing code for data pipelines. Others prefer to design and manage databases. Still others prefer to learn business and build an understanding of the data. The requirements don’t have to be set in stone but a clear description of the company’s expectations allows the data engineer to evaluate the fit and avoid disappointment down the line.

A clear and informative job description repels candidates that aren’t a good fit and attracts candidates that clearly recognize themselves in this role.