Data-Related Roles Exploratory Analysis


Today, I’d like to discuss the job market for data professionals. The job market for these roles is growing and exciting! There are a lot of opportunities and positions for everything data-related.

Suppose you are interested in analytics or machine learning. Or maybe in data science or cloud computing. Whatever your focus is, there are heaps of possibilities and roles on offer.

Now, I am based in New Zealand, so it is only natural to use my country as a small sample of what is out there. Consequently, bear in mind that all the salaries and job frequencies are based out of New Zealand. It’s a great place to be, so reach out if you’re looking to come!

What types of data roles are there?

Though there is a bit of variance in the job requirements for given jobs, there is a rough consensus. I have generalised and summarised the roles! So, here are the top data-related jobs!

Data Analysts

One of the roles in high demand is the data analyst. They are the jack-of-all-trades when it comes to data-related work. Typically, the listings are asking for things like ‘providing insight’, ‘dashboarding’, and ‘data analytics’. They need to know how relational databases work and how to model data. Other top keywords popping up were ‘SQL’, ‘Excel’, and ‘SAS’. They can wrangle data from disparate sources, work out data requirements, and solve all kinds of data-related issues.

Here are some of the top keywords (note: the larger, the more frequently the word appeared):

Aside from a wide variety of skills, they have to know how to understand the business and people they work with. These guys are the ones tying the business people (who typically don’t know data) with the data.

A successful data analyst would have strong technical and interpersonal skills. They also need a good working knowledge of data science and machine learning. The value she can add comes from making the data work and mean something to the business.

Insight Analyst

Now, I have not worked as an insight analyst per se. So I am writing purely from the current job listings. Insight analysts seem to be more involved in supporting decisions than wrangling data. Often, data analysts move to become insight analysts. This becomes possible as they become more familiar with the business and customers, and what kind of business decisions are valuable. When I worked as a data analyst, I contemplated transitioning to an insight analyst too!

The top terms that popped up were ‘customers’, ‘insights’, ‘the business’, and ‘data’. There was a lot of overlap between insight and data analysts. Each company is different, so there is a lot of variation in how these two roles are defined. Here are some of the top keywords for insight analysts:

Data Engineer

Data engineers are the new hot commodity if you will. The number of data engineer roles being advertised outnumber data scientists by about 5 to 1 (based on NZ listings). These are the plumbers of the data world. They take data, move it, and transform it in useful ways. Without data engineers, all the other insight and analysis could not happen (at least not easily). These guys earn top dollar and get to work with some of the newest and most cutting-edge technologies like KafkaSnowflake, and AirFlow.

Here are some of the top key terms that popped up for data engineers:

Data Scientist

This role is likely the most coveted job in the data space. I speculate that it is the prestige and mental stimulus that comes from being a data scientist that draws people to be data scientists.

Often, I heap people imagining being the guy training machine learning models all day long. However, looking at the top keywords for data scientists, there are quite a few mentions of ‘consulting’. Data scientists are often relatively senior, so they spend more time talking to business stakeholders. Hence why data scientists can end up consulting either internally or externally.

How much do they pay?

Data analysts are the starting point with the most extensive salary range and the lowest median. In New Zealand, data analysts are paid a median of $87k with the grand majority of roles paying between $72k and $120k. Most people wanting to get into data science or engineering would benefit from starting as a data analyst first. This will help them get a feel for the business and data requirements simultaneously.

Just up from data analysts are insight analysts. They are paid a little higher than data analysts with a median of $105k compared to $87k. The range, however, is also quite large like the data analysts. I speculate that the insight analysts are paid more because they are experts in the business. They are there to drive decisions that add value to the business, so they get remunerated for it.

Data engineers, being so high in demand, are also offered the most in terms of salary. Their median offer sits at $130k with the majority ranging between $110k and $160k. It is a wide range and there are all levels of data engineers. So, it is still possible to get into data engineering with a little training and a keen interest.

Finally, data scientists! These roles are well-remunerated with around NZD $125k as the median salary. These guys have the smallest salary range compared to the previous positions. However, this may also be due to a smaller sample compared to the number of data analyst and engineer roles being advertised.

Data Roles Cluster Analysis

I was able to see that there were 4 rough clusters; however, the job titles were a mixed bag in each one. Broadly speaking, the left cluster consisted mostly of insight and data analysts with data engineers and scientists on the right.

The cluster at the bottom was mostly lead and senior roles, indicating that seniors typically have different responsibilities compared to other analysts, scientists, and engineers.

Interestingly, there was no clear-cut distinction between the roles among the clusters. There were some similarities and consensus, but hardly data analysts on one side and scientists on the other!

Concluding Thoughts

Whether you are in the back-end serving data as a data engineer or applying machine learning models to predict the next best move for the business, there are roles for anyone interested in this space.

Job titles don’t necessarily mean a specific set of tasks, however. Each business has its own understanding of what each role does. One thing is clear, there is a lot of demand for data skills.

Learn some of the hot-in-demand machine learning skills at QRC and prepare yourself for the next step in your career. Come join us and learn the skills you need for your next career steps!

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