With the rising use of machine learning (ML) and artificial intelligence (AI) in various industries across different departments of the business - we can see how big of an impact ML & AI are making on the world around us. But if you take a pause and double tap on both ML & AI, you will know that they both are only as good as the data we feed into them.
Without good data? Garbage in, garbage out!
Companies today are aware of this. They know that hiring data scientists alone doesn’t solve their data problem. They need a specialist team of data engineers who can build reliable pipelines of high-quality data to perform automated statistical tasks using ML & AI. Companies understand that - to achieve high growth using ML & AI, they need to lay a solid foundation with the help of specialized data engineers on board.
And then there are also companies who are sitting on a pile of data worth diamonds and gold. But this data is scattered across different places. These companies even lack data governance and tight data access controls; making them prone to data theft by hackers.
So, that’s why they need capable data engineers to streamline this data and make the data accessible and useful for the rest of the organization.
Thus, as more businesses move to the cloud and data-driven decision-making becomes a norm, the demand for data engineers is only going up. Companies paying high salary packages to data engineers is nothing but a simple mismatch of supply and demand. And of course, let’s not discount the role of the pandemic which caused great resignation which in turn created more shortage of manpower for the data engineering roles. The recruiters today are having a hard time hiring for specialized data roles.
Post pandemic, a lot of businesses have gone online in the past few years, causing a spike in the jobs in the cloud space, including the demand for qualified data engineers.
And Azure is the number 2 cloud service provider after AWS and thus, of course, there is a lot of demand for Azure Data Engineers from companies running their business on Azure.
Companies often look for specialists roles within the role of data engineering, based on which we can divide data engineers into 2 types:
Before we talk about the roles and responsibilities of a data engineer, it’s important that you understand the biggest application of data - Data Analytics. Data analytics is the process of analyzing the data to optimize different cogs of the business to achieve faster growth. These cogs can be in various forms, including market trends, logistics, etc. The reason why companies invest in data analytics is that they need data to make informed decisions in the various departments of their business. Sometimes they want to gather hidden insights, generate reports, understand customers’ expectations, or make sense of market trends.
An Azure data engineer creates the data pipeline and then designs and executes the management, monitoring, security, and privacy of data using the Azure data tools suite as per the requirements of the business. They are responsible for investigating the underlying data trends and developing the necessary algorithms to make the data more useful to the data scientist team. They have to manage and organize data and also have a look into new trends or oddities that will impact the sales of the business.
Their job is to source, organize and manage the data. So the roles and responsibilities of a data engineer revolve around:
Many candidates who are new to this role often get confused between the role of data engineer and data scientist. The role of a data engineer is to avail the data and the data scientist works on the processing of it. So the data engineer works at the source, while a data scientist (and data analyst) are people who work on the data availed by a data engineer. Any company that has a data scientist or data analyst needs a team of data engineers as well. Data engineers source data as per the requirements of data scientists. Once they know how to find the data, it’s their role to bring that data to their platform in a format useful to the data scientist and data analysts.
Having said that - some companies hire for generalist data engineering roles wherein their work often overlaps with the work of data scientists and data analysts. So when you take on a role like this, you as a data engineer might be expected to also take on the role of a data scientist.
Candidates looking to earn the DP-203 certification are expected to have subject matter expertise in the integration, transformation, and consolidation of data from various formed and unformed systems into a format that can be used for building analytics solutions. Azure data engineers help Azure users understand the data through exploration, to build and manage secure and compliant pipelines of data with the help of specialized data engineering methods.
Azure data engineers employ different Azure services to save and yield enhanced datasets for analysis. They also make sure that pipelines of datasets are high-performing, efficient, organized, and reliable, as per the requirements of the business, and its unique constraints.
For a candidate to be able to clear this exam, he must have a stronghold of data processing languages such as SQL, Python, or Scala, and a deep understanding of parallel processing and data architecture patterns. A potential employer might expect you to fulfill some or all the following requirements in order to be seen as qualified for this role:
Knowing how to code ‘well’ is not mandatory but definitely necessary if you’re serious about taking your career to the next level and earning the handsome available salary. You should have programming skills good enough that anyone can understand and use the code written by you. And that’s not the case with many data engineers who are struggling to grow. They are able to slap a few lines of code and make things work in the short run but only they are able to understand and use the code. If only you are able to use the dashboards coded by you, your impact on your organization will be significantly less and so will be your capacity to negotiate a higher salary package with your employer.
Some companies also expect skills related to AI and Machine learning from data engineers, but if you’re just starting out - you don’t need to worry about these skills at least in your first job. Just keep your focus on improving your programming skills, optimizing workflows, building solid data warehousing, data pipelines with alerts, and overall thinking through the end-to-end lifecycle of the data.
The biggest mistake you can make is trying to learn new technologies all at once, without learning the fundamentals first. For example, if you don’t know Python, or SQL or have not put in enough time understanding data modeling - all the time you spend on understanding these new tools that work on Python & SQL is wasted and only takes you so far. And to be fair to engineers who make this mistake - it’s quite understandable why they do it. There is so much hype around data engineering that it’s luring you to keep adding new tools to your arsenal in the name of making progress.
While this role demands you to be analytical and technical, you can’t go too far if you don’t have the necessary soft skills. Firstly, you need to be able to collaborate with others because data engineers can’t work in isolation. Their work requires them to interact with different departments and teams to be able to map out data requirements and ensure data is of real use to those who need it. Secondly, being a good storyteller and having good persuasion skills can help you move different pieces faster, as you build the data pipelines by collaborating with different departments, teams, and 3rd parties.
You have to earn the DP-203 exam to become a Microsoft Certified Azure Data Engineer. Sometimes data analysts earn this DP-203 certification because they want to upgrade their profile from an entry-level data analyst to a data engineer.
The DP-203 exam certification measures your understanding of the following aspects of Data Engineering:
In the exam, you might have to answer 40-60 questions that may be in formats such as multiple-choice questions, arranged in the correct sequence type questions, or scenario-based single-answer questions. You will have 120 minutes to finish the exam and the passing marks are 700/1000.
No - this certificate just validates your expertise in data engineering but it doesn’t guarantee you a job. To get the job, you will have to get your CV in front of as many employers as you can. If you really want the best job after you earn the DP-203 certification, consider going to local events related to data engineering, and networking with people on Social Media, especially on Twitter.
If you have the time and discipline for self-learning then Microsoft offers excellent learning resources to help you clear the exam, including a clearly laid down learning path here. However, if time is a constraint because you have a full-time job, for example - you’re a data analyst looking to upgrade your credentials to become a data engineer - then self-learning with a parallel job might not be the best strategy to clear the DP-203 exam. While this exam is relatively easy to crack if you’re prepared, there is a ton you need to learn. And as someone who is new to the role of a Data engineer, you might find it hard to know even where to begin.
Thus, candidates who are already working in the field prefer taking instructor-led training like Readynez where they can not only get practical training, but also the necessary mentoring and guidance from industry experts whose sole job is to help them clear the exam. Your course package is designed to provide maximum learning and convenience.
If you have any questions related to this article or the DP-203 exam, please don’t hesitate to get in touch.
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