A Tale of Two Fields: Data Engineer vs. Data Scientist

Exploring the Skills, Responsibilities, and Career Paths of Data Engineers and Data Scientists

Jan 08, 2023

Data engineering (DE) and data science (DS) are two fields that are often discussed in the same breath, but they are actually quite distinct from one another. While both fields deal with data in some capacity, the skills, responsibilities, and career paths of data engineers and data scientists differ significantly. In this article, I’ll explore the key differences between data engineering and data science and provide a comprehensive comparison of the two fields. Whether you’re considering a career in one of these fields or simply want to better understand the distinctions between them, this article is for you. So sit back, relax, and get ready to delve into the world of data engineering and data science!

Introduction

Data engineering is the practice of building, maintaining, and optimizing the infrastructure and processes needed to store, process, and analyze large amounts of data. Data engineers design and build scalable data pipelines, integrate data from various sources, and ensure that data is available and accurate for downstream use.

Data science, on the other hand, is the practice of using data to extract insights, make predictions, and inform decision-making. Data scientists use statistical and machine learning techniques to analyze data, build models, and communicate their findings to stakeholders.

Here are a few key differences between data engineering and data science:

Skillset: Data engineers typically have a strong background in computer science and engineering, and are proficient in programming languages like Python and Java. Data scientists, on the other hand, may have a background in statistics, math, or a related field, and are proficient in tools like Python and R.
Focus: Data engineers are focused on building and maintaining the infrastructure and processes needed to handle large amounts of data. Data scientists are focused on using data to extract insights and inform decision-making.
Deliverables: Data engineers may produce deliverables like data pipelines and storage systems, while data scientists may produce deliverables like reports, models, and visualizations.

Big picture, both practices are working with data and it’s important to look at the hierarchy of people who work with data. Below is an illustration that shows the relationship between different fields that work with data and how they interact and help each other.

data science hierarchy of needs and the jobs that are created based on the needs

I remember the days that data science was a very vague and catch-all title, so it wasn’t clear what people mean by that. However, over time, a lot of areas in the field of data science are separated as a totally different fields. Below is just a simplification of what DS used to entail and what are the branches that are created off of the old DS term. I still think there is a room for more customization.

Skills and knowledge

The difference are briefly mentioned in the introduction, but here I try to expand and give you a detailed comparison of the two field.

Data engineering:

Proficiency in programming languages such as Python, Java, or Scala (ability to develop with advanced functional programming mindset)
Experience with big data technologies such as Hadoop, Spark, and MapReduce (underlying technologies behind pyspark and scala)
Knowledge of database design and management, including SQL and NoSQL databases
Familiarity with cloud computing platforms such as AWS, GCP or Microsoft Azure
Ability to design, build, and maintain data pipelines
Strong problem-solving and critical thinking skills

Data science:

Strong foundation in mathematics and statistics
Proficiency in programming languages such as Python or R (developed mostly in Jupyter notebooks)
Experience with machine learning and data analysis techniques
Familiarity with data visualization tools and techniques
Ability to identify and solve complex problems using data
Strong communication skills to effectively present findings and insights to a variety of audiences

While there is some overlap in the skills and knowledge required for DE and DS, they are generally two distinct fields with their own unique sets of skills and expertise. Data engineers tend to focus more on the technical aspects of building and maintaining data infrastructure, while data scientists are more focused on using data to extract insights and solve problems.

Tools and technologies

Now that we discussed skills and knowledge, let’s discuss how those are manifested in the tooling and what are the tools and technologies that each profession uses. There might be some overlap, but it’s pretty much distinct.

Here’s a comparison of the tools and technologies commonly used by data engineers and data scientists:

Data engineering:

Programming languages such as Python, Java, or Scala
Big data technologies such as Hadoop, Spark, and MapReduce
Data transformation tools like dbt
Data warehouse technologies like Snowflake, BigQuery, Redshift, etc.
SQL and NoSQL databases
Cloud computing platforms such as AWS, GCP, or Microsoft Azure for data access control, data warehousing, etc
Data pipeline and workflow management tools such as Apache Beam, Apache Airflow, or Luigi
Containerization technologies such as Docker and Kubernetes

Data science:

Programming languages such as Python or R
Data analysis and visualization tools such as Pandas, NumPy, and Matplotlib
Machine learning libraries such as scikit-learn or TensorFlow
Collaboration and version control tools such as Git and Jupyter notebooks
Cloud computing platforms such as AWS, GCP, or Microsoft Azure for feature engineering, model training, etc.

As you can see, there is some overlap in the tools and technologies used by data engineers and data scientists, but they also have their own unique sets of tools that are specific to their roles. Data engineers tend to use more technical tools for building and maintaining data infrastructure, while data scientists use a wider range of tools for data analysis, visualization, and machine learning.

Job duties and responsibilities

I think it would be beneficial to take a look at the day-to-day task and responsibilities of these two professions to get an idea about the areas they differ significantly. Here’s a comparison of the day-to-day tasks and responsibilities of DE and DS:

Data Engineers:

Design, build, maintain, and troubleshoot data pipelines
Extract data from a variety of sources (e.g. databases, APIs, raw files) and transform it into a format suitable for analysis
Write code (e.g. in Python, Java, or Scala) to automate data workflows
Collaborate with data scientists and stakeholders to understand their data needs and ensure that the data infrastructure meets their requirements
Monitor and optimize the performance of data pipelines

Data Scientists:

Analyze and interpret complex data sets
Use statistical and machine learning techniques to extract insights and make predictions from data
Communicate findings to stakeholders through reports, presentations, and dashboards
Collaborate with data engineers to access and prepare data for analysis
Continuously stay up-to-date with new data science methods and technologies

In reality, because these two fields need each other, they tend to learn some of the other side’s skills and tools. For example, a data scientist might build a CI/CD for their repo, but in a perfect world, this is a DE/DevOps engineer responsibility.

Salary and compensation

Here are a few points to consider when comparing data engineering and data science in terms of salary and compensation:

In general, data science roles tend to have higher salaries than data engineering roles. According to Glassdoor, the median salary for a data scientist is $121,000 per year, while the median salary for a data engineer is $115,000 per year.
However, salary can vary widely based on factors such as location, industry, and level of experience. For example, data scientists in the tech industry in Silicon Valley may earn significantly more than data scientists in other industries or locations.
In addition to salary, it’s important to consider other factors that may impact compensation, such as bonuses, stock options, and benefits. Some companies may offer more generous compensation packages to data scientists, while others may offer more to data engineers.
It’s also worth noting that salary is just one aspect of compensation, and other factors such as job satisfaction, work-life balance, and opportunities for advancement may be more important to some individuals.

Career paths and advancement

Here are a few points to consider when discussing the different career paths and opportunities for advancement in data engineering and data science:

Data engineers and data scientists often have different career paths, although there is some overlap. DEs typically focus on building and maintaining the infrastructure and data pipelines, while DS are more focused on using data to extract insights and make predictions.

Data engineers often start out in entry-level positions, such as data engineer interns or junior data engineers. From there, they can advance to roles such as senior DE, lead DE, or staff/principal DE. Data engineers may also choose to specialize in a particular area, such as big data or cloud computing. So, don’t get surprised if you hear a data infra engineer role.

Data scientists may start out as data science interns or junior data scientists and then progress to roles such as data scientist, senior data scientist, or lead data scientist. They may also choose to specialize in a particular field, such as machine learning or natural language processing. Many DS engineers have very advanced degrees in engineering, statistics, math or other science majors.

Both DE and DS may have the opportunity to advance to management positions, such as data engineering manager or data science manager. They may also choose to move into related fields, such as data architecture or data analytics.

References

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