Welcome to Agile Recruit’s guide to becoming a Big Data Engineer.
First, let’s look at what a big data engineer does. Essentially big data engineers develop, test and maintain code for Big Data Solutions like Software Engineers. They are involved in a myriad of software development activities including, but not limited to, Database development, Data warehousing, and developing Data lakes. Also, data engineers focus heavily on Data processing and integration tasks such as Extract, Transform and Load (ETL) and developing robust data pipelines.
The amount of data organisations now processes both in batch and real-time calls for some advanced data engineering techniques and tools. So, to become a big data engineer, you must have a foundation of skills to do so.
Where to start
There are two entry points in every career you choose, start working straight out of education. Or re-train / cross-train into new disciplines. We’ll explore both in the hope it will help on your quest to become a big data engineer.
If you want to become a big data engineer straight from education, you must study a relevant degree at University related to computer science or choose a dedicated data-related course. The software and data world is a minefield of academic/theoretical principles and practical software engineering techniques. It will also help your analysis skills and could broaden your career options into roles like a data analyst or data scientist.
If you’re from a different background, like traditional database/data warehousing development background or software engineering, it’s a good idea to consolidate your experience with certifications in relevant data engineering technologies and programming languages. In a way, you’re experienced already in managing data and skills like data modelling or having worked with a data architect is a huge bonus. Understanding data structures and how solutions can tie together in real life is a valuable skill when you want to become a big data engineer.
It’s all about your experience.
Don’t claim you know Hadoop because it is installed on your home PC. Yes, this is valuable in some way, but you must gain experience in big data technologies like Hadoop and other things like NoSQL databases. Whether paid or unpaid, a work placement, sandwich year at uni, do whatever you need to get hands-on experience in big data.
When you start your new role as a big data engineer, you’ll be thrown into the deep end. Without this practical baseline skillset, you’ll struggle. You could be asked to start building data pipelines from day one, which is no easy task without some practical experience. If you can, get some certifications during your time so you can show potential employers your enthusiasm for big data as well as your capability.
You’ll work with large-scale data sets, in some cases, complex multi-layered data solutions that have both structured data in a data warehouse and unstructured data in a data lake. Even a few months experience of working on complex big data solutions, even those that utilise machine learning or artificial intelligence, will put you ahead of the competition.
Online courses
I’ve mentioned it earlier, but it’s a good idea to get some certifications/courses under your belt. Here’s a short list of recognised providers for tech roles with plenty of big data courses:
Now, these aren’t the be-all and end-all, but they can help you in your role as a big data engineer through the experience you gain and also helps consolidate your academic and practical experience.
So, we hope this has helped as a guide to becoming a big data engineer. To summarise, your academic credentials are vital for entry-level roles. For those with some experience in software engineering or data warehousing, it’s more about your transferability and picking up the relevant technologies for the position you’re applying for or simply bolstering your arsenal of data tools and programming languages. Good luck with becoming a big data engineer – you can check out our latest jobs, including data engineering, here.
