8 Career Advice to Become A Big Data Engineer

Becoming a big data engineer requires fulfilling certain skills and knowledge requisites. We look at 8 skills highly sought by recruiters in the big data industry.

Data engineering is a 24-karat gold discipline for businesses and its practitioners today.

It rewards those (businesses as well as professionals) who know how to mine it with high revenues, and handsome compensation.

As a big data engineer, you get to work with the best of tools on latest software. And what’s more, you will be a crucial point of information and success for big companies.

Data Science Council of America (DASCA), a top data science organization summarizes crucial pieces of advice and skills in its insights section that today’s professionals need to become a big data engineer.

To borrow from it, we outline in this blog 10 up-to-date skills and latest information on embarking a winning Big Data engineering career in 2021 and beyond.

This applies not only to aspiring Big Data professionals but also to mid-career professionals looking for career growth and advancement. Read on.

Big Data Engineers: 10 Must-Know Skills and Advice

1. Be a T-shaped professional — a generalist as well a specialist.

Businesses, from all industries and sectors, now seek mavens from big data industry to make right decisions both within and outside their organization — to determine KPIs, for instance in the former, and to stay competitive in the latter.

Across the industry, T-shaped professionals are considered strong hires.

These data engineers are generalists, represented by the horizontal bar on T, and have a know-how of all the concepts including databases, cloud computing, basics of most-used languages including SQL, Python, and others, ETL, etc.

But they specialize with strong skills in one area, represented by the vertical bar in T. So, say a big data engineer is exceptional in Spark or Dask manipulations.

To begin with, a strong-hold on SQL, basic acquaintance with Python, and knowledge of Linux and AWS can easily fetch you a satisfactory junior-level big data job.

2. Get knowledge of working with data on cloud services.

You must know about storage, cloud computing, networking, and database services. If not all, you can learn about one — say AWS or Azure, or Google Cloud — and your learnings can be replicated to a different cloud vendor.

If you are new to cloud services, you can develop know-how from online courses and opt for DASCA’s vendor-neutral big data engineer certifications that focus on the skills relevant to data engineering irrespective of the vendor.

To give an overview of the skills you must gain under this,

- you should be able to download and upload a CSV file

- you should be able to use some Linux basics to spin up SSH

- you should know how to interact with RDS, etc.

3. Learn how to build, manage, monitor, and schedule ETL pipelines

Becoming a big data engineer includes many responsibilities about collected data from different sources, making it appropriate for analysis, loading it to data lake or warehouse (ETL).

In addition to building an ETL pipeline, you should learn to ensure data is reliable, and available. You could learn to use workflow management systems (Perfect, or Apache Airflow) that companies use for managing and monitoring data pipelines.

Knowledge of ETL can position you as a skillful resource and give you a leg up over other candidates in a data engineer job interview. To prove your ability in this, you could gain some experience on Big Data projects at a large company or publish independent works on GitHub.

4. Develop familiarity with containers like Docker and Kubernetes.

Upgrading to new versions sometimes can render your old code non-responsive. Containerization helps in making your code self-contained, and dependency-free. This makes it a much-in-demand skill for data engineering jobs.

5. Know the basics of everything in data.

It follows from the horizontal bar of the T explained in first point. Basic knowledge includes an understanding of everything in big data industry — data lakes, warehouses, REST APIs, 3Vs of Big Data, considerations when migrating to cloud, etc. This portion is very important for cracking interviews as from 100% of the questions asked, at least 60% are from this.

6. Get coding skills right.

Many take this requirement as having a top-notch hand in coding. Rather it’s about being able to write good abstractions and DRY code, and not having to cut-copy-paste all the time from existing codes. In the beginning, it simply means being able to write good functions and understand modularity. When you advance, it would mean delving into packages — creating them or manipulating data in them.

Scala, Java, C, R, Python — are a few in-demand programming languages.

7. Bash Commands and Linux mix.

Develop an ability to work with command line interfaces. Interacting with Linux operating system via bash commands is a crucial skill. Also, learn about the infrastructure as code ecosystem. It includes defining your resources through a declarative language — say Kubernetes YAML files (container mentioned above) and deploying it through command line interface (CLI).

8. Soft skills are as important.

Often ignored, this is also a make or break aspect of many big data engineer job openings. Employers are looking for rounded professionals who can lead teams and take initiatives on their own. Project management, public speaking, people skills, documenting, moderating, and other such skills can be very valuable for your big data job hunt.

Get Hired! Enroll in a data engineering certification.

This was the round-up of top and latest skills that will get your foot in the door for many jobs.

Although recruiters don’t have much preference for the educational level, good credentials do add on points to one’s candidature. Furthermore, with the level of competition, there is they may keep bachelor’s or master’s as basic qualification.

Pursuing and earning an industry certification in big data engineering on top of this can give quite a boost to your résumé. And in case you fall short on any skill or domain knowledge listed above, a professional certification will also fill up that gap.

Among the top-listed and best big data engineer certifications is from DASCA. They are vendor-neutral and encompass all-suite of brands and software. It offers two rungs of data engineering certifications –

- Associate Big Data Engineer (ABDE): For beginners, or junior professionals

- Senior Big Data Engineer (SBDE): For senior professionals

Their exams are 100% online, and learning resources are also provided by the certifying body. Other than DASCA, other certifications include those from Microsoft Azure, Amazon, Google Cloud, etc.

Have an add-on? Congratulations!

If you are someone who keeps the pulse of industry trends and understands the guiding business principles for the big data industry, you are already set to rise high in the domain.

To get started, be prepared for some basic technical questions and assignments like star schema.

Most importantly, believe in yourself and it will take you to places.

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AI Researcher, Writer, Tech Geek. Working on Deep Learning & Neural Networks. #cobots #coding #algorithms