According to a 2019 Dice Report, the demand for data engineers grew by 88% between 2018 to 2019 and this is just the beginning as organisations are hungrier than wolves to hire data engineers in 2020. The shortage of skills is leading to a high demand for data engineers with average salaries shooting upto INR 785,438, according to Indeed.com.  With handsome pay-checks and companies itching to hire skilled data engineers, becoming a data engineer is a practical choice. So, you might now want to know how to become a data engineer and what are the best steps to take to build a high-salary career as a data engineer. The journey to a six-figure salary as a data engineer is long and is no easy feat but definitely attainable. Post reading this article, you will have a perfect roadmap on how to become a data engineer.

How to Become a Data Engineer – The “A-Z” Guide

To the outside eye, a data engineer’s job role is somewhat confusing, complicated and inaccessible. So, to shed some light on one of the most interesting and fascinating data science job roles, we’ve put together the ultimate introduction to what is data engineering and who is a data engineer. 

What is  Data Engineering?

Data engineering is the backbone of data science that emphasises the practical applications of data collection and analysis. Data engineering is on the front line of the data strategy of an organisation. The term “engineering” as we know it is the art of using technology and science to build systems that solve problems. Similarly, data engineering deals with the application of science and technology to overcome any data handling problems and data processing bottlenecks for data science projects. You can think of it in terms of Lego Building Blocks, it would not be possible to build a Lego Castle (successfully complete a data science project) without Lego blocks (data engineering). It’s a fact that data engineering is a foundational and one of the most critical skills in a data scientist toolbox. Ideally, data engineering is a close cousin of data science.

Who is a Data Engineer?

You will find multiple answers to this question, as there is no commonly accepted definition for a data engineer job role. However, most data engineers would agree that, in a general sense, data engineering is the art of building and maintaining the data pipeline systems of an organisation. And data engineers ensure clean, reliable, and performative access to these data pipelines. Data engineers use database querying languages like SQL scripting languages like Python and various ETL tools to build the architecture and infrastructure for data generation. Data engineers can work with a small RDBMS for a mom-and-pop business or something as big as an exabyte data lake for one of the Fortune 500 companies. Data engineers are usually the first hires on a big data team in most of the organisations.

‘A scientist can discover a new star, but he cannot make one. He would have to ask an engineer to do it for him.’ — Gordon Lindsay Glegg

It seems like these days everybody wants to become a data scientist but have you ever thought that a data scientist is as good as the quality of the data they are endowed with. This is where data engineers job kicks in. A data engineer is responsible for building data pipelines, workflows, and ETL processes for transforming data to effectively support the requirements of the data scientists and data analysts in an organisation. A data engineer is a hybrid of sorts between a data scientist and a data analyst who wrestles with data to advance the data science goals of an organisation. Data engineer might sound like an unsexy job title but they are the unsung heroes of the data world and super-critical to the success of a data science project.

For instance, imagine an e-commerce company that sells diverse types of washing machines on their website. Each time a user to the e-commerce website clicks on a particular washing machine (be it front-load, top-load, automatic, semi-automatic, etc), a new piece of raw data is created. A data engineer is responsible for defining how this data can be collected, what kind of metadata needs to be stored for each click event,  and how the data can be stored for easy access. Now for the collected data to be analyzed for predictions, it needs to be well-organized and cleaned. A data engineer is responsible for choosing a reliable and easily accessible data warehousing system(Google Cloud, Panoply, Redshift, etc) for storing the organised data. 

How to Become a Data Engineer – The 3-Step Guide

There is no one-rule-fits-all path to becoming a data engineer. Most of the data engineers acquire data engineering skills on the job, through self-learning, or through project-based online courses. However, when you’re looking for information on how to become a data engineer, there are three important things you need to keep in mind
1. Educational qualifications
2. Learning path to acquire the desired skills,
3. And, experience.
With all three steps in place, the path to becoming a data engineer will be more like a bed of roses. As the popular saying goes, to reach the top of the ladder you have to climb every single step on the ladder with dedication and commitment; the same is the case with landing a top gig as a data engineer. Having said that, let’s take a closer look at each of these three steps –

Step 1 – Earn a Bachelor’s Degree to become a Data Engineer

A data engineer is a specialised position that typically requires a Bachelor’s degree in Mathematics, Statistics, Computer Science, Information Technology or a related field. However, if you have not graduated from one of these fields, ensure that you take some online courses on algorithms and data structures, database and data warehouse management along with some basic programming sessions.

A data engineer’s job doesn’t necessarily only refer to having a traditional degree, as you can also learn how to become a data engineer without a degree through various online resources free or paid. Regardless of your degree, the most important thing that you need to do is ensure that subjects like IT, statistics, and math are in line with your interests and hobbies. Trust me, data engineering will be your cup of tea then. 

Step 2 – Acquire Core Data Engineering Skills

A data engineer must have knowledge of database tools and querying languages like SQL, practical experience working with distributed systems like Hadoop, Spark, Kafka, and have hands-on experience with one or more programming languages like Python or R that help with statistical analysis and modeling. However, this is not an exhaustive list because data engineers need to acquire various other skills in operating systems, basic machine learning concepts, etc. If you are a beginner, initially the learning process might feel a little overwhelming but remember it is not possible for everyone to know everything. It is important to learn how to acquire relevant data engineering skills when needed because acquiring skills is a career-long process in any job role. With that being said, here are key skills that you need to acquire to pursue a career as a data engineer –

  • Solid understanding of core data engineering concepts like Data Ingestion, Data Synchronisation, Data Transformation, Data Modelling, Data Governance, and Performance Optimisation.
  • Data engineers need expertise in one or more of data engineering specific programming languages like Java, Scala, Python or R to write ETL scripts, build data pipelines, set up statistical models for analysis and create dashboards.
  • In-depth knowledge of database management is required to collect, store and retrieve data from databases in real-time. As a data engineer, you need to know how to work with diverse database platforms both SQL and NoSQL databases like Couchbase, MongoDB, Cassandra, and others.
  • Ability to design and build data warehouses.
  • Knowledge of distributed systems and big data tools like Spark, Hadoop, Kafka, Pig, Hive, etc has become an essential requirement of most data engineer job descriptions.
  • Solid understanding of at least one operating system like UNIX, LINUX, or Solaris is recommended to make the data pipelines tick successfully.

Data management tools and technologies are constantly evolving, so keep an eye on the latest happenings and innovations and acquire those skills when needed.

 Step 3 –  Gain Experience 

The project-based learning approach is the best way to gain experience as a data engineer if you are a newbie. Consider the fact that many data engineers usually acquire specific data engineering skills by learning on the job as and when they get to work on diverse projects. Choose projects that interest you and make a note of skills you would want to learn from a project. As you progress through each module of the project, you should have learned at least 50% of the necessary skills to complete that module. This is the most efficient and practical way of gaining experience as a data engineer. Publish blogs on medium about the projects you have worked on and create your project portfolio on GitHub to showcase your skills to potential employers.

Take the First Step… Start Today!

Hopefully, this content piece has illuminated the skills, education, and responsibilities of a data engineer. Data engineers and data scientists are red-hot in the job market with something rewarding on offer for everyone. Whether you are a fresh graduate in your 20’s or a 40-year old experienced professional in the IT industry – if you have the right mindset, you can become a data engineer by acquiring relevant data engineering skills. Remember, anyone can become a data engineer. The most difficult part is getting started with the learning process, so choose an online data engineering course and dip your toes in today. At Springboard, we are excited to share with you our Data Analytics and Data Science online programs that come with 1:1 mentoring, project-based curriculum and career coaching along with a job guarantee that will help you make a career transition into these future technologies. Do check out these courses, trust me – you won’t look back.