The most in-demand big data roles explained
By Kelly Dent
Big data seems to be the phrase on everyone’s lips—no matter where in the world you are or what industry you work in.
What might have once felt like a bit of a buzzword now represents one of the most lucrative tech markets to date.
The digital skills gap affecting the tech industry means that demand for experienced cloud and big data professionals is now higher than ever, with companies in fierce competition to recruit the best talent out there.
With businesses of all sizes striving to efficiently capture, process, and analyze these colossal data sets, there is no shortage of career opportunities available in the big data ecosystem.
No matter which career path you set your heart on, you’ll definitely need to have a special set of skills at your fingertips to turn swathes of unstructured data into actional insights.
One of the most exciting things about working in tech today is the sheer range of specialisms to choose from. So which big data job is the one for you?
In this post, we’ll take a look at some of the hottest jobs available in the big data world, and the skills you need to excel in each one.
Big data analyst
Arguably one of the most desirable big data roles right now, experienced big data analysts are hard to come by and are therefore massively in demand. A big data analyst needs to have a solid, working knowledge of key technologies like Apache Hadoop, Pig, and Hive to name just a few.
If you’re interested in this type of big data role, you’ll need strong analytics skills as well as a background in statistics and algorithms to be able to source relevant information from datasets. You’ll also need to know domains like the back of your hand.
Big data architect
As far as soft skills go, you’ll need to be a strong leader, able to work with and mentor others while building solid relationships with vendors, partners, and key company stakeholders alike.
Big data engineer
Big data engineers are in charge of developing, testing, implementing and maintaining the big data solutions created by architects in an organization. This type of role isn’t just super popular, it’s very much in demand all over the world too. When it comes to skills, you’ll need to know the ins and outs of data architecture and the various tools that it involves, as well as SQL, data warehousing and ETL, Hadoop-based analytics, coding, machine learning and the usual operating systems. As a big data engineer, here are a few of the responsibilities you can expect:
- Creation and maintenance of analytics infrastructure
- Development, testing, and maintenance of architectures (e.g. databases, large-scale processing systems)
- Creation of dataset processes for use in data mining, modeling, acquisition, verification
Data scientists are basically the analytical and technical wizards responsible for crunching numbers and extracting juicy insights from an organization’s data sources. They take structured and unstructured data, interpret it, and transform it into useful, actionable business insights.
Typically, a data scientist would need to know how to use the same technologies common to other big data roles, but exactly which ones depends entirely on the employer and their specific operational setup. Data scientists will generally know how to:
- Get the Hadoop ecosystem up and running
- Perform queries against the data stored within
- Extract the data and house in a non-relational database
- Take non-relational data and extract it to a flat file
- Wrangle data in R or Python
- Engineer features after some initial exploratory descriptive analysis
Data visualization developer
On a day-to-day basis, data visualization developers are generally in charge of designing, developing, and supporting any data visualization processes. One of the most important new fields in the industry today, data visualization developers also prepare the information extracted from big data and present it in a way that is easier for those beyond the world of big data to understand and interpret. Here are the types of skills you should have if you’re pursuing this much sought-after role:
- Impeccable analytical skills
- Strong communication skills (written and oral)
- Excellent mathematics skills
- Critical thinking
- Keen eye for detail and identifying trends/patterns
Machine learning engineer
Machine learning (ML) forms a crucial part of the big data landscape and, together with artificial intelligence (AI), is en route to becoming one of the most sought-after skills in tech for years to come. ML engineers develop valuable data analysis software which allows organizations to run components autonomously, removing any need for human supervision over these processes and saving a lot of time to boot.
Some of the most important duties involved in this role are:
- Running ML tests combining a specific programming language with ML libraries
- Deploying and maintaining ML solutions
- Ensuring solutions provide optimal performance and maximum scalability
- Establishing good data flow between database and backend systems
- Implementing custom machine learning code
- Analyzing data to devise use cases
Business intelligence engineer
Business intelligence (BI) engineers are masters of reporting tools; they use those tools to query and manage an organization’s vast data warehouses. They work with BI analysts to transform data into accessible reports and intel that help an organization make business-critical decisions.
To really thrive in this kind of role, you’ll need top-notch analytical, problem-solving, and consulting skills, with a good understanding of business functions and quality assurance methodologies.
Business analytics specialist
A business analytics specialist uses their expertise to help develop and test scripts, carry out crucial business research to identify and understand any issues and devise solutions that’ll save time and money in the long term.
This role does share a lot of the same responsibilities as data analysts, but analysts tend to be more involved in business management as opposed to pure analysis.
Big data developer
A big data developer has tech like Apache Spark or Hadoop at their fingertips and can process parallel data like it’s nobody’s business. You’ll need to know all about NoSQL databases and be familiar with programs such as Pig, Hive, and Map Reduce, specifically how to grow, optimize and debug them. When it comes programming languages, the big data dev in the making should focus on Scala, Python, and Java. In big data developer roles, you’ll find yourself working closely with big data analysts, specifically applying ML algorithms to data.
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