Data Engineering has a Branding Problem

For careers that work primarily with data, the term “data science” is a dominant buzzword, and there is no doubt it is one of the fastest-growing fields—but its recent attention seems to be misplaced.

Data scientists aren’t the only professionals who ensure companies can capture and take action on the valuable insights data has to offer.

In fact, there are three key roles in effective data teams: data engineers, data scientists and data analysts.

While many companies regularly recruit data scientists and data analysts, the precursor—data engineering—seems to be a forgotten role, and that’s a big problem.

Why? Well, it starts with understanding the purpose of a data engineer.

Data engineers collect relevant data then move and transform this data into pipelines for the data science team.

The data engineer role is foundational to the success of data projects and yet it’s the least considered when companies are looking to expand or hire new data team members.

So, it seems the attention attached to data science has come at the incorrect time, the data engineers should have come first.

Data engineering is suffering from a branding problem.

How to Know Your Company Needs a Data Engineer

As companies begin examining their data, they start with hiring a data scientist, but later find out their new hire is missing a collection of valuable skills that are more associated with a data engineer.

Additionally, their architecture is not at the proper point to even begin data analysis; it is scattered about, lacking structure and lacking any type of proper aggregation or pipeline.

These are the tasks a data engineer excels in, not a data scientist, and this strikes at the core of the issue.

The important question to ask before you consider where you’re at in the data hiring process are:

Do we have data readily available?
Are we getting enough data?
Is the data clean, valid, maintained, and is there a way to organize it?

If the answer to any of these questions is no, then you might be in the need of a data engineer rather than a data scientist.

These two are essential in a well-rounded data team and oftentimes should be hired in parallel.

Data engineers typically have these skills:

• Data warehousing solutions
• ETL pipelines
• Visualizing large data sets
• Designing experiments
• Relational databases (SQL)
• Non-relational databases (NoSQL)
• Data APIs
• Machine learning
• Data filtering and optimization
• AWS
• Hadoop
• Apache Spark, Apache Airflow, Bash, NumPy, python, pandas, git, PostgreSQL, matplotlib and more!

So, What Should You Expect from a Data Scientist?

The data scientists use the data captured and cleaned by the engineer to then analyze, test, and provide insights from that data.

Data scientists thrive on the technical challenge of building large-scale, complex systems and sophisticated models to improve performance.

Data scientists clean and process enormous amounts of data in order to find patterns or trends typically to make better business decisions.

Here are some of the skill sets you could expect from a data scientist:

• Automating tasks and analyses
• Acquiring data from varied sources
• Data exploration
• Data wrangling
• ETL pipelines
• Visualizing large data sets
• Designing experiments
• Relational databases (SQL)
• Non-relational databases (NoSQL)
• Distributed computing
• Training machine learning models
• Deploying models
• Monitoring model performance
• Working in the cloud
• Deep learning models
• Natural language processing
• Time series analysis

Why Do You Need A Data Analyst?

Data analysts may sift through the same data sets as data scientists, but their key responsibility to deliver the results of the models and predictions to other people who make business who product decisions.

Data analysts understand the value of creativity AND strategy. They may or may not have the expertise of a statistician or programmer, but they do know how to interpret results, make recommendations and implement data-driven strategies.

Often, that decision-maker is not as data-savvy, so the data analyst must explain their results in a non-technical way, which introduces an additional layer of complexity to the job.

Data Analysts typically have these skills:

• Effectively analyze, communicate and present data to key stakeholders
• Make better decisions by building predictive models
• Extract, clean and analyze data
• Create clear and strong data visualizations

Back to the Branding Problem

Companies jump ahead to hiring data scientists before data engineers not because they value the work more, it’s simply a branding problem.

And the easiest way to course-correct this is to bring awareness to this essential role that is missing on many data teams.

If your company is ready to hire, we’ve created an in-depth hiring guide including interview questions, salary overviews and strategies to assess data project.

Download Your Copy

Then, consider joining The Data Incubator as a hiring partner.

We train the best data scientists, data engineers and data analysts available today – with the latest tools and technology they can apply to your data from day one – and ensure they’re ready to use their skills to enhance businesses like yours.

Become a Hiring Partner