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Data Roles – Friends but not the same

With the explosive growth of big data & data analytics, several new roles have emerged in the software industry. Being that there is significant overlap between roles in terms of tasks and responsibilities, the data-related career landscape can be confusing. To understand how the various roles in the data field differ, it’s helpful to look at the relevant skills and technologies used to complete data projects and achieve organizational goals in each position.

With existing data roles evolving and new specialized positions being developed at the speed of technological advancement, those interested in entering the data field may understandably have trouble determining where they will best fit in. In this article, we will explore the most common data career archetypes and provide advice on how to get closer to landing your dream job in data.

To start, let’s look at this graphic which provides an easy way to identify data roles by answering questions about whether certain job functions are applicable:

Data roles identified by questions related to their duties.

 

 

A data strategy offers another way to identify data roles based on the specific skills and technologies required to successfully perform each job. How does this work?

Essentially, a data strategy is a plan or roadmap used to propose what should be done with data, including how the particular data needs to be accessed, shared, and managed. It also involves defining the tools and tasks needed throughout the three main stages of a data project:

The three main steps to complete a data project.

 

 

Data Transformations

In a data project, analytical techniques such as statistics, math, and data mining allow us to convert initial raw data into a cleaned and structured data set. Once the data has been organized, it can then be analyzed to help answer business questions or formulate a new hypothesis. Ultimately, the end goal is to extract actionable insights from the data and create an impact.

How is data transformed to make a decision?

 

Still have questions? Don’t worry — this was just a brief introduction. Now, let’s dive into a more detailed comparison of the roles of data analyst vs. data engineer vs. data scientist.

 

Data Roles – What They Do

What Is a Data Analyst?

A data analyst extracts information from a given pool of data through several methodologies, including data cleaning, data conversion, and data modeling.

The two most important techniques used in data analytics are descriptive or summary statistics and inferential statistics. Visualization techniques and tools are also commonly employed by data analysts.

Implementing data analytics allows organizations to efficiently process queries and produce actionable results in a short period of time. 

What Is a Data Engineer?

A data engineer specializes in preparing data for analysis. They are focused on how to properly read and format the data as well as how to scale the application to handle a large volume of data, maximize platform resilience, and ensure the data remains secure.

Data engineers usually have a software engineering background and experience using programming languages like Java, Python, and Scala. Alternatively, they might have a degree in math or statistics that helps them apply different analytical approaches to solve business problems.

Their primary objective is to help other users turn oceans of data into valuable and actionable insights.

What Is a Data Scientist?

This role emerged more recently. Data science is currently a hot topic, with many companies looking to hire individuals with the corresponding skill set.

It’s important to note that data scientists are not simply statisticians. While a statistician is interested in building a model that shows the relationship between one or more variables and a result, a data scientist seeks to go further. Their aim is to predict the future as accurately as possible using the data at their fingertips.

Some of the skills required to succeed as a data scientist overlap with those needed to become an effective data engineer, but this doesn’t mean that the roles are interchangeable. Both data engineers and data scientists are programmers to an extent; however, data engineers tend to have a better grasp on programming, whereas data scientists are more focused on data analytics.

Data Roles – Responsibilities

For a Data Analyst:

  • Analyzing data through descriptive statistics.
  • Using SQL to retrieve and manipulate information.
  • Communicating results with the team using data visualization.
  • Working with the management team to understand business requirements.

 

For a Data Engineer:

  • Developing and maintaining data platforms.
  • Conducting testing on large-scale data platforms.
  • Handling error logs and building data pipelines.
  • Handling raw and unstructured data.
  • Supporting data architecture used by data scientists and data analysts.

 

For a Data Scientist:

  • Performing data preprocessing, which involves data cleaning and data transformation.
  • Using ML tools to forecast and classify patterns in data.
  • Increasing the performance and accuracy of ML algorithms through fine-tuning and performance optimization.
  • Understanding business requirements and formulating questions that need to be addressed.
  • Using robust storytelling tools to communicate results with other team members.

 

Data Roles – Required Skills

For a Data Analyst:

  • Strong statistics and math aptitude.
  • Knowledge of SQL, Excel, and data visualization.
  • Problem solving attitude.
  • Proficient communication of results with the team.

 

For a Data Engineer:

  • Knowledge of programming languages such as Java, Python, and Scala.
  • Solid understanding of operating systems.
  • Ability to develop scalable ETL packages.
  • In-depth knowledge of SQL and NoSQL.
  • Knowledge of data warehousing and big data technologies.

 

For a Data Scientist:

  • Proficiency in math and statistics.
  • Ability to work with structured and unstructured information.
  • In-depth knowledge of tools like R, Python, and SAS.
  • Knowledge of SQL and NoSQL.
  • Familiarity with big data tools.

 

Conclusion

 

While there is clearly quite a bit of overlap when it comes to the required skills and responsibilities for the various data roles, these jobs are by no means interchangeable. Thus, you’ll have to make a decision and choose to specialize in one of them specifically. In any case, you can expect an extremely positive job outlook and a lucrative career in data!

To sum up everything we’ve discussed above, we’ll leave you with this handy diagram that showcases the keywords associated with each data role, along with commonalities in job functions:

Skills and Responsibilities Overlapping between Data Roles

 

 

Ready to join the Data team at Wizeline? We have open positions!

We are growing and we are actively looking for Data engineers eager to grow and develop their potential and build a career at Wizeline.  

 

Apply or share our open roles: 

Data Engineer Mexico based remote

Data Engineer Colombia based remote  

Data Engineer LATAM based remote 

Data Engineer Spain based remote 

 

 


Ana Cristina González Villicaña

Posted by Ana Cristina González Villicaña on June 1, 2021