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