Data Analyst vs. Data Engineer: A Full Comparison

Do you want to become a data analyst? Or maybe you dream about being a data engineer? Can't make up your mind? I'll help you. Read about the two roles’ history, their market situation, and the skills you need to become one in this data engineer vs data analyst comparison.

You already know that it's a good idea to enter the data world and work with databases. But how to do it? Behind which door is the better career waiting for you? Which path will offer a chance for greater success and a more stable future? In this article, I will compare data analyst and data engineer roles. I will consider their salaries, how easy it is to find a job, what skills you may need to become one, and what to expect.

I want to help you make a choice that may be crucial in your career and then see what steps you should take to achieve your goals.

Are you ready? Let’s start our comparison!

Data Analyst vs. Data Engineer: Two Ways to Work with Data

Organizations both produce and rely on data more and more. As the world becomes digitized and connected, the speed by which we generate data is accelerating. This vast amount of data brings challenges, however. We need to collect, store, and maintain it for use now and in the future. And we need to understand data and translate it into value.

To tackle these challenges, we need data engineers and data analysts. But what are these roles, and what should you expect if you become one? Understanding the key differences between these roles can help you make an informed career choice.

This section describes what data analysts and data engineers do, what problems they solve, and the demand for them.

First, let’s start with a short history of each profession. It will help you better understand the current situation.

A Brief History of the Data Analyst and the Data Engineer

I can't fit the entire history of data analytics, data science, and data engineering into such a small space. Instead, I’ll focus on the changes that led to them. This will help you understand the significance of this trend and assess its future relevance to your career.

Perhaps the most famous early definition of data analytics comes from John Tukey, who separated data analysis from statistics. He emphasized the practical side of analysis (versus theoretical work) and computation’s role in it.

Following that, we can see a succession of innovations and concepts that both advanced data analytics and opened up new opportunities:

• 1970s – The invention of relational databases and SQL made it easier to store and process data.
• 1980s – Progress in databases and the development of this branch of science led to the creation of data warehouses optimized for data reporting, analysis, and business intelligence.
• 1990s – The rise of data mining methods enabled people to go beyond ‘manual’ data analysis and use algorithms to cluster data and make predictions
• 2000s – The emergence of Google’s web search and the publication of their MapReduce technology made it possible to process qualitatively bigger datasets stored in distributed systems.
• 2010s – Cheap and easy-to-use cloud services (like Amazon’s Redshift and Google’s BigQuery) made Big Data and machine learning accessible to individuals and small businesses.

In this history, we can see that the concept of a data analyst existed quite early on. However, the last two stages led to the emergence of the data engineer.

Compared to data engineering, data analysis may seem like an old leftover from a previous century. Nothing could be more wrong. Data analysis is still one of the key issues related to databases and, as indicated by market trends, it is not going anywhere. It will be crucial in many areas, from finance to marketing, for a long time to come.

In mid-2021, we can add one more thing. Our digital and data-based services became critical in how we could respond to the COVID-19 pandemic. Without this infrastructure, it would have been much harder to manage social distancing, risk assessment, and an effective response to the virus’ development. Again, this suggests that analyzing and working with data will become an even more important job in the future.

Now that we have a historical context about data analysis and data engineering, we can drill down to each role.

Data Analyst

As a data analyst, you tap into data and extract insights from it to produce value for your organization. A key part of this role involves statistical analysis to identify patterns and trends in the data. You transform business problems into the language of data, and you shape your findings into a meaningful form. You often generate reports and visualizations.

Data Analyst Jobs

Below, you can find a list of example job posts from Glassdoor.

Source: Glassdoor

Typical industries where you can find data analyst jobs are finance, healthcare, and marketing. However, you can find data analyst jobs in any domain where there is data and where insights are valuable.

Data Analyst Career Path

Based on PayScale’s report, data analysts can continue their career as a senior data analyst or get into an analytics management position. Alternatively, you could pick up additional skills and become a data scientist.

Data Engineer

Data engineering is a relatively new concept that combines database management with software development skills.

It gained lots of attention during recent years, and this interest doesn’t seem to be stopping.

Source: IT Jobs Watch

As we use more and more data, the demand for data engineers is rising.

As a data engineer, you build and maintain the data infrastructure within your organization. You plan and build database schemas, connect data sources, and optimize data pipelines. Data processing is a critical part of ensuring data reliability and accuracy. You also ensure the seamless flow of both structured and unstructured data. Data engineers are also specialists in database management systems, ensuring that data is organized and accessible.

Data Engineer Jobs

Again, based on a Glassdoor search, you can find data engineer jobs in industries where data is abundant and essential. However, companies need data engineers, especially when they are analyzing and managing their data in-house.

Source: Glassdoor

Data Engineer Career Path

PayScale shows that data engineers can continue their career as a senior data engineer or as a data engineer manager. Or you could retrain yourself to become a data scientist or software engineer.

Data Analyst vs. Data Engineer: Earnings Comparison

The next step you want to consider is the salary you can expect in these roles. How well do these positions pay?

According to PayScale, data analysts earned around $67,500 annually on average (as of June 2024).

Source: PayScale

The same metric for data engineers was around $97,000.

Source: PayScale

Please note that job sites show salary numbers based on their methodology and the geography they cover. Salaries depend on many factors, like location, industry, the specific skills required, and the years of experience you have in the job. For example, the following chart shows the estimated pay changes for experienced data analysts.

Source: PayScale

And here is the same information for data engineers:

Source: PayScale

They show a smaller difference between the salaries of data analysts and data engineers in the first years of work.

We should also keep in mind how titles work for engineering roles. You can keep the title of data engineer for many years but gain qualifiers solely based on your years of experience. As a data analyst – similar to other non-engineering roles – you get a new title after many years (e.g. in management).

Skills for Data Analysts and Data Engineers

Both roles can require a wide range of skills. Fortunately, PayScale shows us the most used ones. Let’s review them for each role.

Skills for Data Analysts

For data analysts, not surprisingly, the most critical skill is data analysis, followed immediately by SQL.

Source: PayScale

Depending on the specific role and industry, data analysts often also work with Excel, perform statistical analyses, and generate reports from databases. Excel fans, are you there? I have an important message for you: Most of the things you do every day in a spreadsheet are much easier to do in databases with SQL. Interested? Check out the article Still Using Excel for Data Analysis? See Why SQL Is Better! for more info.

Overall, data analysis is a great career path for someone with SQL and data analysis skills.

Skills for Data Engineers

For data engineers, SQL is the most popular skill. As your tasks overlap with software development, however, you also often use Python. Want to grow your arsenal of Python skills? Look no further – here is our sister site, LearnPython.com, with the courses you need.

Source: PayScale

Besides these two technologies, the primary skill for data engineers is ETL (Extract, Transform, Load) – i.e. how to move data from one place to another effectively. Based on this, data engineering can offer you a career path with SQL, Python, and engineering skills like ETL and Spark.

SQL

Data engineer or data analyst, the most important skill is probably SQL, so let’s discuss it in more detail.

SQL is not a new skill. However, its long-term and consistent popularity shows that it is reliable in many situations. Examples range from simple data storage through backend services to third-party uses like Google Analytics.

Moreover, learning SQL is a great idea even if you do not become a data analyst or data engineer. Such a skill is useful for managers, marketers, financiers, accountants, HR specialists, and in many other roles. It's just a very useful thing. As a result, there are many great career paths with SQL.

Mastering SQL, of course, is not a short process. However, with the right approach, you can pick it up effectively, even if you are a beginner.

At the end of this article, you will find tips about how you can learn SQL quickly and effectively so you can get into a data analyst or data engineer job.

Data Analyst vs. Data Engineer: What Are the Differences?

So far, you’ve learned about what data engineers and data analysts do, the problems they solve, and their respective demand and salary figures. Both roles can also lead to careers as data scientists, who are in high demand at top companies. You also learned about the essential skills they require.

The infographic below is an overview of the differences between data analysts and data engineers:

Overall, both roles provide great opportunities and can lead to fulfilling career paths. As the need for good data and good insights is increasing as we speak, the demand for these roles will probably follow.

The real question is which direction you want to take and how you can start.

Deciding Between Data Engineering and Data Analysis

If, after reading this article, you are still wondering which path to choose, our suggestion is to pick one! The best way to find out which role is for you is to try to do them.

The lucky thing is that the fundamentals of the two roles are almost the same: you need to know how to query and handle data with SQL, Python, or another means. So, after you have worked on one path, you can still pivot to the other.

For this reason, we advise you to check out courses you can apply right away. Whether you want to get insights from your data to support decisions or to build full-blown reports from it, we have you covered!

Of course, if you have already decided, you can skip this step and take a fast and direct path. In this case, we can help you to become an SQL expert or a database specialist.

What will you choose? Maybe there are still arguments that I missed for and against either role? Please share them in the comments.