Data Analyst vs Data Engineer

In the modern world, more and more data is constantly being generated. And that means there’s an increasing demand for professionals who know how to collect, organize, and analyze this data.

Some of the most popular careers in tech are data-focused: data scientists, data analysts, and data engineers are just a few of the titles that earn impressive salaries, desirable benefits, and lead to lasting career growth. This article takes a closer look at the roles of data analysts and data engineers to give you a clearer picture of these two professions.

What Is Data Analysis?

The purpose of data analysis is to answer the question, “what is the data trying to tell us?”

In a business setting, data analysis is becoming indispensable, as it provides insights about customers, competitors, and business operations. Using that knowledge, organizations can make informed decisions on how to take their business forward.

Take the example of a casino. By analyzing the data every casino machine is generating, casino owners can find the answers to questions like:

• Which games are being used and which aren’t?
• What are the most popular games?
• What times of the day are they being played?
• What is generating the most profit or loss?

Once they have all of this information, casino managers can choose the best course of action to adjust relevant aspects of the casino, ultimately leading to greater business revenue and growth.  

Clearly, data analysis is a highly sought-after skill across many different industries. It forms the core of many desirable tech roles, including data engineering and business analysis.  

What Is a Data Analyst?

A data analyst gathers data, organizes it, and uses it to reach meaningful conclusions. Data analysts collect and store data on market research, sales numbers, logistics, and other behaviors. By using their technical expertise, they ensure the quality and accuracy of the data. They then use it to identify facts and trends that are then processed, designed, and presented in a manner that helps business stakeholders to make better decisions.

As a data analyst, you need to be able to scrutinize information using data analysis tools like Apache Spark, R Programming, and IBM SPSS. As an example, on a day-to-day basis, you might be using an advanced computerized model to retrieve the required data or removing corrupted data.

If you prove yourself as a data analyst, you may well move up the ranks to become a data engineer.

What Is Data Engineering?

Data engineering is the less famous cousin of data science, but it’s no less important than data science or data analysis. Data engineering focuses on the practical applications of data collection and analysis. It provides the mechanism for collecting and validating the information that data scientists and data analysts use to answer questions.

Data engineering is akin to a combination of software engineering and business intelligence, with big data abilities such as knowledge of the Hadoop ecosystem, streaming, and computation at scale.

Data engineering is slowly gaining traction in the autonomous vehicle segment. It’s data engineering that enables self-driving cars to make decisions such as making a turn, recognizing traffic and road signs, interpreting the actions of other vehicles and pedestrians, and choosing the best route.

What Is a Data Engineer?

Essentially, data engineers transform data into a format that is ready for analysis. These professionals are usually software engineers by trade. Unlike data analysts, their job involves the compilation and installation of database systems, scaling to multiple machines, writing complex queries, and strategizing disaster recovery systems. Their job also involves creating data set processes used in modeling, mining, acquisition, and verification.

As a data engineer, you need to have a solid knowledge of common scripting languages and tools such as PostgreSQL, MySQL, MapReduce, Hive, and Pig.  You need to be able to use these skills to continuously improve data quality and quantity.

Data engineering roles can be broadly classified into three kinds:

Generalist: Employed in smaller companies, where they are among the few ‘data-focused’ individuals in the organization. Their job is to take care of all the steps involved in data processing, from managing data to analyzing it.

Pipeline-centric: Commonly found in mid-size companies with complex data science needs. They work closely with data scientists and help transform data into a useful format for analysis.

Database-centric: Larger organizations need experts to manage the flow of data, and that’s where data engineers come in. Database-centric engineers work with data warehouses across multiple databases.

How Is a Data Analyst Different from a Data Engineer?

Below is a quick guide to the differences between each role.

Data Analyst

• Involved in translating numerical data into an accessible format.
• Must have a good understanding of tools such as Microsoft Excel, SAS Miner, SPSS, and SSAS.
• Usually has some knowledge of SQL, Python, R, and JavaScript.
• Experts in data munging, data visualization, exploratory data analysis and statistics.

Data Engineer

• Involved in preparing data for operational and analytical purposes.
• Must have a deep understanding of programming languages such as SQL, Java, SAS, and Python.
• Must be proficient in frameworks such as Hadoop, Pig, Hive, Apache Spark, MapReduce, NoSQL, and Data Streaming.
• Experts in developing large data warehouses using extract transform load (ETL).

Data Analysis or Data Engineering—Which Pays Better?

Data Analysts make $69,467 per year on average. Depending on your skills, experience, and location, you can earn anywhere between $46,000 and $106,000 per year. The national average salary for a data engineer, on the other hand, is $112,288 a year.

Depending on their skills, experience, and location, a data engineer can earn anywhere between $76,000 and $165,000 a year. Those with greater levels of experience can earn an average salary of up to $172,603 a year.

How to Begin a Career as a Data Analyst

To begin an entry-level data analyst job, you'll probably want to get a specialized education in data analytics, whether it's through a traditional degree program or a data analytics bootcamp. For higher-level data analyst positions, companies usually ask for a master's or a doctoral degree in data science, business analytics, or a related field.

Along with gaining the tech skills of data analysis, you should also think about developing your Interpersonal skills, team work, and written and verbal communication skills, as hiring managers will be looking for all of these in your resume and interview.

You can fast-track your entry into the field with a bootcamp, such as Thinkful’s full-time Data Analytics program. This course prepares you with all the skills you need to get hired as a data analyst, business analyst, data engineer, and much more.

How to Begin a Career as a Data Engineer

A certification or data analytics bootcamp will help prepare you to enter this competitive field with relevant skills and an impressive portfolio.

Key skills for a data engineer include:

• In-depth knowledge of SQL and other database solutions
• Knowledge of data warehouse architecture and ETL tools
• Coding and machine learning skills
• Familiarity with various operating systems
• Strong presentation skills

Next Steps

Both data analysts and data engineers are in high demand, so choosing between the two will come down to your personal strengths and interests. If you enjoy creativity along with programming, you should opt for data analysis, as you’ll be required to represent your cleaned data in new ways. If you’re fond of math and enjoy working with complex data and decoding, you should choose data engineering.

You might still be undecided between the two professions, and that’s ok: take your time to choose the right path. Read up about all the exciting career potential in these and other tech fields. And if you’re considering a bootcamp to achieve your career goals, read more about our students’ outcomes.