The New Skills of the Data Engineer in 2021

It’s cliché to say that technology is evolving fast—but that’s because it’s 100% the truth.

This rapid transformation is ubiquitous and all-encompassing, impacting society in almost every conceivable way.

Thus, jobs in this sector must keep up with this lightning-quick evolution. Almost nowhere is this notion more evident than in the role of a data engineer: the skills required in this position continue to shift with the times. 

Read on as this blog highlights the most essential attributes to possess as a data engineer heading into 2021:

Knowing Your Numbers

Any data engineer should be immersed in numbers. Thus, math skills are in high demand, especially in these disciplines:


·         Medians, mode, maximum likelihood indicators, standard deviation, and distribution are all integral concepts you’ll encounter in this role.

·         Mastering sampling techniques helps negate any experimental biases.

·         Predictions are a significant part of your role:

o    Expertise in inferential statistics bolsters your accuracy with predictions.

·         Charts and graphs are required to tell your data story, which falls under the “descriptive statistics” umbrella.


·         Probability-based skills are undoubtedly valuable for any data engineer, which include:

o   Bayes theorem

o   Probability distribution functions

o    Limit Theorem

o   Expected values I II

o   Standard errors

o   Random variables

o   Independence

 When performing statistical tests, understanding probabilities allows you to accurately pinpoint trends and patterns.

Linear Algebra:

·         Algorithms you’ll use in your work rely on linear algebra to function optimally.

·         Those who spend more time working with machine learning will benefit from understanding matrices and vectors.

Multivariate Calculus:

·         Mean value theorems, gradient, derivatives, limits, the product/chain rules, Taylor series, and beta/gamma functions are all pivotal to data engineers.

·         These concepts aid you with logistic regression algorithms.

·         Hiring managers might present you with calculus problems during the interview process.

Python Fluency

For data engineers, the general rule with programming is becoming fluent in Python. 

This comprehensive, object-oriented language seamlessly deploys itself in apps and websites. Furthermore, Python’s data science community makes it a highly appealing addition for tech organizations.

Python libraries are worth exploring as you develop your skills in the language. With it comes reusable code that can be repurposed to streamline basic actions. 

Data engineers should focus on the following Python libraries:

·         Pandas

·         NumPy

·         Matplotlib

·         SciPy

·         Seaborn

·         TensorFlow

·         Scikit-learn

Machine Learning 

As a tech company continues to grow, the likelier it is they’ll deal with growing influxes of data. Thus, engineers should prioritize obtaining a vast knowledge about machine learning as if you don’t already have one.

Familiarizing yourself with the following terms and concepts will enhance your ability to get the most value from big data: 

·         K-nearest neighbors

·         Random forests

·         Ensemble methods

Are you late to the party when it comes to machine learning?

You’ll need to gain expertise in concepts such as regression, classification, decision tree, and anomaly detection modeling. Furthermore, you’ll need to have an in-depth knowledge of recommendation systems, time series prediction models, and how to select the correct model.

A suggested first step toward learning about these subjects is Springboard’s Machine Learning Career Track.

Data Wrangling

Transforming and mapping data that you’ve gathered is a valuable skill. You’re compiling this information from an array of sources, and it’s all over the place—making it uniquely challenging.

With strong data wrangling skills, you’re using coding language to handle imperfections. It’ll help you manage missing values, string formatting, and date formatting. 

One working example with date stamps is how specific days can appear differently: 

E.g., “2020-05-06” or “05/06/2020” must be consistently transformed for all entries to ensure analysis occurs without any hiccups.

Synergy Systems

Seeking out data engineer candidates with cutting edge skill sets? Contact Synergy Systems for access to a diverse, top-performing talent pool.

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