00:00What's going on, everybody? Welcome back to another video.
00:02Today, we're going to be taking a look at the difference between a data analyst and a data engineer.
00:12Data engineering has definitely gained a lot of traction, become a lot more popular in the last
00:16year or two, and so it's good to take a look at the differences between these two jobs.
00:20So let's not waste any time. Let's jump onto my screen and take a look at the difference.
00:24Now, we're going to take a look at a lot of different areas, but the first thing that we're
00:26going to look at is the responsibilities of a data analyst and a data engineer.
00:30Let's start off with the data analyst. So the data analyst is going to clean and analyze static data.
00:35They're going to perform statistical and exploratory analysis. They're going to create reports and
00:39visualizations and work directly with stakeholders to make data-driven decisions. So really what
00:44they're doing is working with data that's in a database or an Excel file or wherever that data
00:48is being stored. A data analyst is going to go and clean that data to make sure that's very usable,
00:51and then they're going to create those reports and visualizations and give those to the client.
00:56Now let's take a look at data engineer. First thing they're going to do is build and maintain
01:00ETL pipelines. They're also going to collaborate a lot with data analysts and data scientists to make
01:05sure that the data is available for them to use. They're also going to design, construct, and
01:09maintain the data infrastructure. And lastly, they're going to ensure data quality and accuracy.
01:14The data engineer is going to create automated processes to get data from the data source
01:19all the way into their native databases. Not only that, they need to create the databases and the
01:24schemas around it to store that data properly and efficiently. And of course, they're going to work
01:27with data transformation and data cleaning as well. Data analysts and data engineers tend to work
01:32together quite a bit because data analysts rely on the data that is in the databases or at the source
01:37in order to do their job. So oftentimes they're working with the data engineers to make sure that
01:41the data is correct and to make sure that it's being cleaned properly. The next thing that we're going
01:45to take a look at is the technical skills that data analysts and data engineers use.
01:48Let's start out with a data analyst. So for a data analyst, they're going to use SQL, R, and Python.
01:53And within Python, they're going to use some specific libraries and packages like pandas,
01:58polars, numpy, and matplotlib. They'll also use data visualization tools like Tableau and Power BI.
02:03Of course, they're going to use Excel and then probably a cloud platform like AWS or Azure.
02:08And then lastly, they might use some type of statistical tool like SAS or SPSS.
02:11Now let's take a look at the data engineer. So for the data engineer, they're going to use SQL or
02:15even
02:16no SQL. They're going to use Python, but they might use some other packages and libraries as well.
02:20The similar ones that they might use are ones like pandas and polars for using that structured data,
02:24but they may also use another one like Airflow. And there are some others, but Airflow tends to
02:29be pretty popular. They may also use a cloud platform like AWS or Azure. They may use Java,
02:34Hadoop, Spark, and Excel. One thing I wanted to specifically note in here is that they both may use
02:39AWS and Azure, but just in different capacities. A data analyst on Azure is most likely going to be using
02:44the databases or things that they need to access the data. Whereas a data engineer might use
02:48something like Azure Data Factory and data warehousing and data lake and a few other things
02:52as well. In general, data engineering tends to be quite a bit more technical. They're running a lot
02:56more code. Whereas data analysts, they definitely do code, but it's just not as much or not as advanced.
03:01The next thing that we're going to take a look at is the education. So let's go ahead and take
03:04a look
03:05at the education for a data analyst. So for data analysts, you're typically going to need a baseline
03:09of a bachelor's degree. There are a few jobs that do require a master's degree, but even those,
03:15you know, it's a little bit flexible. So you still can have a bachelor's degree with some experience,
03:19or there are some that actually do require you to have a master's degree, but they're just far and
03:23few between. Some degrees that are really popular amongst data analysts are ones like statistics,
03:27mathematics, economics, computer science, business analytics, and information systems.
03:31So those are some of the common degrees that you'll see people get when they're trying to become a
03:35data analyst. I myself came in through healthcare. So I have a degree in recreational therapy,
03:39which has nothing to do with data analytics, but I was able to use my healthcare experience and then
03:44transition. But now let's take a look at data engineering. So you're going to need either a
03:48bachelor's or a master's. Some of the common degrees that you might get are ones like computer
03:52science, engineering, software engineering, information systems, and information technology.
03:57I made one addition to the data engineering because in data engineering, you can get certifications,
04:02whereas you can get certifications for data analysis, but it's just not as popular and not as needed.
04:07It actually can make quite a bit of difference if you are a data engineer. So I added certifications
04:11here. You can get certified as an AWS or an Azure data engineer. Those actually do carry some weight
04:16when you're applying for jobs. One other thing that I want to note is you don't absolutely have to have
04:20a bachelor's for either of these jobs. For a data analyst, you don't need it because, you know,
04:25if you have some experience, if you have the skills, then you can get a job. Although it's just a
04:29little bit more difficult. I think it's even more difficult to get a data engineering job without a
04:33degree, but it is possible. I know people who have done that. It's just not as common for data
04:38engineering than data analytics. Next, let's take a look at some job titles that you might see for a
04:42data analyst and a data engineer. Let's start off with the data analyst. You're going to see job
04:46titles like data analyst, quantitative analyst, technical analyst, and then you'll have more
04:50domain specific titles like healthcare, finance, and marketing analyst. Now let's look at the data
04:55engineer. You'll see data engineer one, two, three. You also see big data engineer, ETL developer,
05:01and lead data engineer. At some companies, you may even see one like database developer,
05:05but sometimes those are completely different jobs. So it really is dependent on the actual job
05:10description. The very last thing that we're going to take a look at is the pay or the salary of
05:13a
05:14data analyst and a data engineer. Let's start off with a data analyst. So for a data analyst at the
05:18entry level, it'll be 50 to 75,000. For a mid-level, 70 to 90,000. Then at a senior
05:23level,
05:24anywhere from 80 to 120,000. For the data engineer at an entry level, you're looking at anywhere from 65
05:29to
05:30100,000. For mid-level, 85 to 120,000. And then for a senior level, 100 to 160,000. Now
05:37these are
05:37just averages across the board. Of course, there are many factors that could play into it, like your
05:41years of experience, where you actually live, and a lot of other things as well. So that is the
05:46difference between a data analyst and a data engineer. The data analyst tends to be more client
05:50focused, working with actually analyzing and cleaning the data, and then providing some type of
05:54report or visualization. The data engineer tends to be more on the backend side where they're collecting
05:59the data, getting the data from the source, and making sure that that data is available for the
06:03product or the report or, you know, whatever the data analyst or data scientist is wanting to use it
06:07for. So I hope that that was helpful. If it was, be sure to like and subscribe below, and I'll
06:11see you in the next video.
