Big data is hot right now. High demand, juicy paychecks, high impact work, and sexy titles mean thousands of people are looking for careers in this field.
But if you’ve spent any time Googling data jobs, you’ve probably stumbled upon these three roles and then got confused:
At first glance, you might think they do the exact same thing. Don't blame you there.
So if they aren't the same... then what the heck are the differences and most importantly, which of these roles would best fit your career goals?
Because let's be honest here. You don't want to waste hours learning new skills and applying for roles that aren't suitable. Worse still, we wouldn’t want you to land your “dream role” and then realize it’s not what you imagined.
Well don't worry because we're going to cover all this and more.
No coding or tech experience? Don't sweat it.
Here at ZTM, we know that anyone can learn to code and learn the skills required to get hired working in any Data related role.
But let's not get ahead of ourselves, let's first dive in to figure out what role we'll help you get hired in 😉
The biggest factor between businesses that fail and succeed is their ability to collect, understand, and then act on data.
Many companies (small and large) usually fail to track or understand their data, but not the best in class market leaders. The best perfoming companies are data driven.
Data driven? Meaning they make the majority of their decisions based on what their data tells them - regardless of personal opinions.
All the FAANG companies are extremely 'data-driven' and their incredible performance helps highlight just how important this is.
So better collection, understanding, usage of data = more revenue and more profits for companies.
So companies are hiring more and more people to fill these 'data' roles. And it isn't slowing down any time soon. Demand for people with these skills will continue grow.
So let's dive into the 3 main data roles and what skills you need for each.
Each of these 3 roles we're about to cover are vitally important. Without them, the data world would be in chaos!
But there are important differences between the roles so let's start with a simple overview before we dive deeper into each:
Data Engineers are the architects of data ⚙
They set up all the tracking and tools to ensure that data pipelines run smoothly and the information is collected and sent to the right people.
Not only that, they also clean that data and remove any errors to make sure it's ready for use by other teams.
Data Analysts are like the detectives of data 🕵️♀️
Data Analysts take all that raw data collected and organized by the Data Engineers and turn it into something useful so that other people in the company can understand that information and make decisions based on it.
You could say they bring the data to life. They make it visual. It's common that they will create charts, tables, and graphs to help us see what it all means.
They're also going through all the data to try and figure out what's actually important and determine the signal from the noise.
That's why they're like Sherlock Holmes but better since they can use SQL, Python, or R.
Data Scientists are the fortune tellers of data 🔮
But instead of using crystal balls, they use complex algorithms and statistical models.
Data Scientists use a variety of methods and tools to help forecast predictions and run a wide variety of scenarios and simulations based on the data.
They can then advise management what actions they recommend taking based on the results.
Clear as mud? Good!
Now let’s dig deeper into each role, what you'll be doing in each position, what you'll need to learn for that role, and how much they pay.
The role of a Data Engineer is all about setting up the systems to ensure everyone else has reliable data to use.
As they saying goes... "garbage in, garbage out". If you don't have clean data (ex: no errors, no duplicates, etc.), then everything else is useless or even worse... might be leading you to wrong conclusions.
Nothing else is possible without the Data Engineer.
There are 4 main areas that Data Engineer's work on:
Being familiar with cloud-based data storage and processing solutions like AWS or Google Cloud is also a plus.
According to zip recruiter, the average salary for a Data Engineer right now is $122k per year.
As for job opportunities, there are currently 472,788 Data Engineer jobs in the US alone.
Data Engineers often study computer science, computer engineering, or a related field, simply because they need to understand how systems scale.
But that's definitely not a requirement. However, you will need to make sure you do learn and understand data structures and algorithms.
Many Data Enginners don't start in this role though. Usually they have prior experience working as Software Engineers or in similar roles but with a focus on data management and analysis before moving into this position.
If you're interested in building and maintaining the infrastructure that supports data analytics.
It's also an excellent option if you are interested in and enjoy coding and building systems.
A typical career path for a Data Engineer is starting as a Junior Data Engineer or Analyst, progressing to a Senior Data Engineer, and eventually becoming a Lead Data Engineer or Architect.
It sounds complex but it's actually easier than you think to get started. Lucky for you, we have multiple courses to help you such as our Complete SQL + Databases Bootcamp and Complete Python Developer Bootcamp course.
The Data Analyst role is all about understanding and preparing data to be sent on to other people.
Some examples of tasks that a Data Analyst might do include:
Netflix is a data-driven company.
Not only do they use it to predict business related like... "how many customers will cancel if they increase pricing by 10%".
They also use data to help them decide which movies and TV shows to create and which actors to hire based on what their data models tell them is likely to be most popular.
Imagine that Netflix wants to use data to find the movie genre to invest in next year.
Well, if you were a Data Analyst, they could hire you to interpret that data and create visualizations to communicate your findings to Data Scientists or team leads.
Because of the semi-social component, Data Analysts need to not only be technically savvy but they also need to be able to communicate with other teams.
They must also know statistical techniques to disentangle data and find insights.
According to zip recruiter, the average salary for a Data Analyst right now is $71k per year.
As for job opportunities, there are currently 697,193 Data Analyst jobs in the US alone.
This role tends to be the most common entry point into a data career.
The traditional Data Analyst background is someone who has some experience in math, statistics, economics, finance, or other related fields.
In addition, they have experience working in business or finance and understand their industry well. Great analysts also have excellent domain knowledge.
If you're interested in working with data but don’t have a strong background in programming, then a Data Analyst role may be a good fit for you.
It's also a great option for those who enjoy working with data but want to avoid getting too deep into coding or math. Instead, you want to be a bit closer to using data to help solve business problems.
A typical career path for a Data Analyst is starting as a Junior Data Analyst, progressing to a Senior Data Analyst. From there, you can be a specialist as an individual contributor or choose to pursue a role as a manager or team lead.
Finally we have the Data Scientist, who acts as the advisor for data-driven decisions.
There are 3 main tasks for a Data Scientist:
Note: We are generalizing with a lot of this. Things can change from company to company. For example, some companies also task Data Scientists with acquiring and cleaning the data, which would traditionally be an engineer's or analyst's task.
As you'll no doubt notice, there is a fair bit of overlap between a Data Analyst and a Data Scientist, usually because you might not have both on a team and the Data Scientist may have to fill in certain tasks such as acquiring, analyzing and collecting insights from data.
This is especially true at smaller companies that may not have the resources to hire an entire data team. Instead, they hire someone that can take on the key skills of all 3 roles.
However, the key difference between a typical Data Analyst and Data Scientist is the task of forecasting and predicting results, and then advising clients, senior management, or team leads on recommended actions to take.
(The Analyst would prepare the information but it's likely the Data Scientist who will give advice on what to do with it).
Data Analyst. "Customer A has ordered 4 Uber eats meals per week for the last 3 months".
Data Scientist. "If Customer A continues to order 4 per week, they'll spend $X on average, while also consuming Y calories. I predict that if you follow this path they will gain Z lbs. Likewise, if you were to remove 1 of those meals, you would start to lose X lbs".
There are a variety of technical skills a Data Scientist likely has.
As mentioned, you may be required to collect and clean data using tools like Python and R.
Additionally, you may be required to know how to use time series forecasting techniques to make predictions on future demand.
Finally, you need to be able to interpret the data and find insights, before providing these insights to the business stakeholders.
And because you'll probably be talking to non-technical people, you also need to make sure that you have excellent soft skills and communication abilities.
According to zip recruiter, the average salary for a Data Scientist right now is $119k per year.
As for job opportunities, there are currently 310,592 Data Scientist jobs in the US alone.
As you can see, there is high demand for all types of data roles.
As for their background, Data Scientists often come from math, statistics, or computer science fields, but they could also come from any other background, providing they acquire and prove they have the right skills.
In fact, here at ZTM, we help people go from any background to being able to get hired as a Data Scientist by providing them with a step-by-step career path roadmap to follow.
It's a start to finish path to go from never writing a line of code, to being hired making over $100,000 / year. Talk about life changing!
This role is a great option for those who enjoy using data to make predictions and discover insights.
Especially if you like digging into the weeds to understand why things happen, and enjoy advising and communicating with others.
Hopefully I haven't scared you away from what I believe is an amazing, rewarding career.
The best part is we can help you get there no matter wich data role is the best fit for you.
Maybe you're interested in building and maintaining the infrastructure that supports data analytics. In this case, our Complete SQL + Databases Bootcamp and Complete Python Developer courses are the perfect place to start.
Are you more interested in combining business with data but don't have a strong background in programming? Our data analyst courses can help you start on this path, including Python for Business Data Analytics & Intelligence and Business Intelligence with Excel.
Or are you more interested in using data to make predictions and discover insights? If so, our full Data Scientist Career Path would be the perfect option for you.
Whatever your choice, remember that each role has its own set of responsibilities, skills, and benefits, so consider your own interests, skills, and career goals to decide which role fits you best.
The world of data is vast and exciting, and the data industry is constantly evolving. There's a high demand for professionals in these roles, so now is the perfect time to start learning!