Data Engineer Jobs are Better than Data Science Jobs

Data Engineer Jobs are Better than Data Science Jobs

Ever since Harvard Business Review chose the term ‘data scientist’ to be the sexiest job of the 21st century, it sure has been a joyful ride for data scientists.

However, with time, things have changed.

Data scientists are in tough competition with data engineers. Although it is quite likely for both the job roles to have overlapped responsibilities, the role of a data engineer is more focused on building data pipelines and transferring data into pipelines for data scientists.

All in all, data engineers are accountable for three major tasks - design, build and arrange the data pipelines for data scientists to further analyze, test, aggregate, and optimize data.

Data scientists have always been getting all the credit while data engineers lay the groundwork for most projects. As in, maintenance of the data pipeline has been one of the most important tasks for data engineers. Perhaps, it is high time to turn things around.

Data engineering jobs at their best

A data engineer is responsible for building data pipelines that are further used to transfer data from one data source to a data warehouse. They basically gather, generate, store, and process the data in batches or in real-time.

To get into data engineering jobs, the candidate needs to have experience in programming languages like Python, Scala, C++, Java, knowledge of SQL databases and ETL (Extract, Transfer, and Load), and software engineering.

The data engineer roles and responsibilities:

They are occupied with managing and organizing data while keeping a keen eye for the latest trends or inconsistencies taking place in the current industry. Besides the technical skills mentioned, data engineers also need to possess soft skills to communicate insights to business stakeholders. Below are the common tasks and responsibilities a data engineer does daily:

  • Design and develop big data infrastructure
  • Prep up the data for analysis
  • Align the architecture according to business requirements
  • Build and optimize data pipeline from the ground up level
  • Develop dataset for processing
  • Facilitate different ways to boost data reliability, quality, and efficiency
  • Make use of larger datasets to address business concerns
  • Detect hidden trends and patterns using data
  • Automate manual processes
  • Redesign infrastructure for higher scalability
  • Stay in sync with the data scientists to help them optimize products

While a data scientist is responsible to find solutions with the gathered data, data engineers are responsible to do the tough work. Data engineers and data science jobs are growing at a rapid pace. The only drawback is that organizations are still finding it a challenge to recruit relevant candidates with the latest skills.

Data engineer salaries

The average salary compensation data engineers earn on a yearly basis is USD 137,776, according to Glassdoor. However, this could range anywhere between USD 110,000 to USD 155,000 according to the skills, location, and relevant experience.

A senior data engineer ideally makes around USD 172,603, with a reported salary ranging anywhere between USD 152,000 to USD 194,000.

According to PayScale, a data engineer with the following core skills are likely to earn a certain percentage hike in their salaries, let’s have a look:

  • Scala: 17 percent
  • Apache Spark: 16 percent
  • Data warehouse: 14 percent
  • Java: 13 percent
  • Data modeling: 12 percent
  • Apache Hadoop: 11 percent
  • Linux: 11 percent
  • Amazon Web Services (AWS): 10 percent
  • ETL (extract, transform, load): 7 percent
  • Big data analytics: 6 percent
  • Software development: 2 percent

If you’ve planned on launching a career in the big data domain, then perhaps you also need to know the type of job roles you’re likely to get into. According to Dataquest, below are the three types of data engineer roles you’ll probably be suitable for:

  • A generalist: These type of job roles is typically seen in smaller companies having smaller teams. For such roles, the candidate needs to wear more than one hat, as in performing multiple tasks since they are the main people who’ll be handling every data-related problem. Generalists are accountable for being involved in each and every step during the processing of data. A generalist role is perfect for those i.e. data scientists looking to transition from data science to data engineering jobs.
  • Pipeline-centric: These job roles are often found in mid-size companies wherein data engineers work closely with data scientists for further analysis of the data that is gathered. Data engineers in this role hold expertise in managing distributed systems.
  • Database-centric: Database-centric jobs are perfect for data engineers looking to work in larger organizations. Such a job role requires the candidate to work full time managing the flow of data while their major focus will be on analytics databases. A data engineer working in such type of organization is responsible for working with data warehouses across more than one database.


Data engineering is among the topmost growing sectors across the industry. With job roles in demand and lucrative compensation, earning a career in this field could keep your career secure.