GIS Salaries and Skills REVEALED using REAL data

now a lot of you might have faced these same issues if you look for a job in geospatial or GIS from the research that I've done there is no single comprehensive source of information on the skills that you need to get into a GIS career or for the salaries that you can expect going into different roles in the gis or geospatial Fields a lot of times when you look at different sites for skills that you need to enter into the gis field you'll find a lot of listings that look something like this a lot of lists of generic skills but no information on specifically what you need to do or more importantly any data to back that information up now you might find some salary ranges on different sites but there's sometimes issues with the number of samples that they might have or the ranges are incredibly wide about the data that they're actually collecting so how do we fix this how do we figure out the skills that really matter for modern GIS in the job market today how do we understand what some of the salary ranges that you can expect are and what are some of the different titles that you should be focusing on based on what you want out of your geospatial career now the answer is pretty simple we need some data to actually look at this and that's exactly what this video does looking at real information from real job postings to understand what you need to learn what the skills you need are and then ultimately some of the information that you can expect when going into a job search now before going forward I have to take a little bit of a pause and say a tremendous thank you to one person who really helped make this possible what up Dad nerds I'm Luke lupus is another Creator here on YouTube you can check him out in the links below and he actually had a very similar idea which was understanding and collecting data on the job market within the data analytics space now he did a lot of work to create his posts and he actually analyzed way more listings than I did roughly around six hundred thousand of that the really cool part about this is that the job postings that he's pulling are updating on a regular basis there's live data coming in and he created a whole data pipeline to stream and clean and really update this data he also posted all the code to the airflow Pipeline on GitHub so my first step was actually to take that Pipeline and actually pull some data for myself in one of my previous videos I shared a sample of this data that I pulled and Luke actually found it and was gracious enough to take some time to pull all of the data listings that he had that focused on GIS or geospatial career so really thank you Luke without you this video would not be possible and all the hard work you put in so in this video we're actually going to be analyzing two different data sets in parallel and I think there's actually some interesting insights to be pulled from both of those so all the data comes from the serp API which is basically an API to interact with different Google searches the specific API that we use looks at Google job listings which aggregate different job postings from across the web so it does a pretty good job of sampling across different salary jobs as well as hourly or freelance jobs on upwards so in my first data set I used Luke's original pipeline to pull jobs that are in the United States and include the keywords GIS or geospatial and pulled this data over about three months and I ended up with about 1300 different postings that I analyzed for this video look with through and filter down to the jobs that had GIS or geospatial in the descriptions and that ended up with about 7 300 different listings and those are actually from locations all over the globe to analyze the data I use Google Cloud to actually analyze the data from the original pipe so all my data is stored in bigquery and I I can just query that with simple SQL statement so to analyze the second data set which Luke shared I actually wanted to reuse a lot of my code and my SQL statements so I actually pulled this into a new tool that I found in a recent conference called duckdb I like Duck DB a lot because it's a really small package it's not a lot to install especially in Python all I have to do is run pip install.db and I basically have a database that's ready to go now the advantage here with duckdb is as data gets larger it's really easy to extend that to much larger data sets so this analysis is broken into four categories first is understanding the job titles that are in the posting second is taking a look at if the jobs require a college or university degree the second is looking at the skills that each job requires and then finally taking a look at some salary information so let's start with the first section looking at the titles for these different jobs so from the smaller sample of just GIS or geospatial jobs the thing that stood out was the diversity of job titles I had to go through and try to categorize each of the job titles and pull different text out of them to really understand and group them together but but the most striking thing was the sheer amount and diversity of job titles in GIS and geospatial there were a few that Rose to the top like you can see here which is analyst specialist technician consultant but beyond that there were so many other Thai title how do you understand what these roles do how they interact with different roles and what other supportive roles you really need to create a foundational modern GIS practice and I guess most importantly for someone entering the space or going into a new career how do you understand which role I should take and then the ultimate career path that you'll go down once you enter into that role I think the broader data space has already gone through this with the emergence of data science data engineering and data analytics to create clear titles that everyone knows what each person's role is how they interact and how they work together personally I think geospatial would benefit from consolidating the number of titles that we have creating clear career paths for individuals going into the field and ultimately understanding how these roles interact together ultimately having too many roles that are too similar is really not beneficial for either party for the organization trying to build the gis practice and for the individual entering the career field now when I look at the data that Luke provided he really focused on three different roles that was Data engineering data scientists and data analysts I think what's really interesting is that you see the data scientist at the top of this listing now spatial data science has been growing quite a bit and I think it's an emerging field and the boom and data science has definitely helped bolster this part of the practice in geospatial when I look at data analysts I definitely see some clear parallels to a GIS analyst but it'd be interesting to break those two apart to see what the difference between a data analyst doing geospatial and the roles that they do and data engineering seen that one grow quite a bit is really important because as more data comes in I think having people that know data engineering skills and are able to practice that with geospatial is going to be really important too next let's take a look at with the job postings required some mention of any degree now this could be a degree of any level bachelor's Masters associate Babs it really doesn't matter now in the data that I pulled I found it incredibly interesting that this was almost split down the middle 57 of the postings that I took a look at did mention a degree ultimately I think this is a good thing having more jobs that open it up to people that have geospatial skills that maybe learn them through non-traditional routes I do believe that geospatial education is really important and I'll talk about this in the conclusion of the video but maybe this is an indicator of how we can create new learning Pathways to help educate and bring new tools to Modern GIF after looking through Luke's data the numbers are a little bit closer where seven percent actually did not require any sort of degree so the next big section is jumping into the skills that came along with these job postings now the tricky part is that a lot of this data is buried into the job description itself and I filtered out some of that data into my data set and Luke already did a lot of the hard work there so once again thank you for doing and cleaning all of that data to make it really easy for me to make this video so after categorizing and going through all the free tax job descriptions through the date post that I pulled this is the day that was returned back so I tried to categorize this as best I could for things like SQL I included terms that had post GIS or postgresql in addition to SQL the language itself now a few things really stand out to me when I take a look at this list the first is that spatial SQL has climbed up right behind traditional desktop tools now personally I know the value of spatial SQL I've talked about it a lot I'm working on a book that talks all about spatial SQL so this is really interesting for me to find out and actually see that across a lot of different job types and job titles is very very interesting development the second is that qgis actually tends to go much lower now this could be because of the job sample that we have or that they're primarily looking for those with Enterprise skill sets but qgis being so low is another interesting development there the other is that python is actually falling behind Excel now I found this really interesting especially with the rise of data science But ultimately this might tell me a few different things first is at the gis analyst space or specialist space which is as you can see from the titles that we pulled a big chunk of the sample data from those listings is that python has not quite grown because it has not caught onto that data science wave in addition to data scientists lots of data analysts and data Engineers use Python to perform their work so given that Trend I would almost expect python to grow and change over time but as of now it's actually quite lower even lower than Excel the other one I like to call out is to see that cloud is almost at 20 in terms of the number of listings that require this as a skill and my guess is if you looked at this data from a couple years ago Cloud would be much lower on the list but I suspect that cloud is going to continue to grow as more and more tools are moving into cloud-based workflows now when you look at the skills required from the job postings that Luke shared with me you can see that the numbers look a little bit different as well as the ordering of the different skills Python and SQL really make up a one-two punch of core tools and languages that make up a modern data stack it's equal to queer data and perform analytics as well as python to orchestrate workflows and perform analysis or machine learning now as you can see here business intelligence or bi tools like Tableau or power bi are much higher on the list compared to our geospatial or GIS only sample bi tools are followed shortly thereafter by traditional GIS tools so as I kind of expect there's a blend of how people are using traditional GIS tools as well as desktop business intelligence tools the other thing that's really interesting to me here is that qgis is almost three times as higher in this sample than the gis only sample I don't want to speculate too much but this could be because this audience is open to more open source tools and I personally think that this indicator more people requiring qgis and potentially using qgis in the space is indicating an interest in geospatial analytics more broadly hey quick Interruption so all the charts you're seeing here are from a report that I pulled together in notion with all the data it's in the description below so if you want to check it out and play around with the date a little bit go ahead and head over there I also have a link to a notion template that I use to keep pretty much everything in my life organized from work to home to just literally about everything if you want to check it out that link is in the description as well and now back to our regularly scheduled programming and now to the final topic which is salaries now there are a lot of different job sites that provide information on salaries and even between these sites you're going to find differences in the ranges and the medians of those data points with these sites I found two main problems the first issue is some of the sample sizes can be quite low they might not have enough data to get a really accurate number or that might be skewed in One Direction or the other which then leads into the second problem which is the wider range of those data points now not all the job postings in either of the data sets had salary data or information in them in the first data set there were 75 listings that had some sort of salary information and listing and in the second data set I found that there were 242 listings that had some sort of salary information now in the first data set there was actually a pretty even distribution of salaried versus hourly jobs so I tried to break that apart here so you could kind of compare the differences as you can see here in the first data set the average listing is just under seventy five thousand dollars per year with the max being around eighty nine thousand dollars and the minimum being around sixty two thousand dollars for the listings with an hourly rate the average listing there is just under thirty two dollars the max is around thirty eight dollars and the minimum is just close to twenty six dollars so I want to break this data out two other ways for the salaries the first is buy skill and the second is by role type so while it's really tricky to understand what skills correlate to which salaries what I was able to do is take these salary listings and compare them to the hourly listings and understands where skills stood out between those two data sets and the main point that I found was that programming languages specifically SQL and python had a much higher percentage rate in the salary jobs compared to the hourly rates and the traditional GIS tools stay pretty much the same between the two now when you break this apart by roll we see some different Trends here as well jobs with the developer title led the pack with the highest average salaries around ninety one thousand dollars followed by GIS specialist around sixty five thousand dollars NGS analysts around sixty three thousand dollars and bring up the bottom of the list is a raw GS technician which brings in around fifty two thousand dollars now some of these Trends carry over into the second data set with the data that you can see here so you can see here the average salary is right around a hundred and sixteen thousand dollars with the maximum average being around 136 000 and the minimum being around ninety nine thousand dollars so when we break these down by scale we can see some similar Trends to before but since we have a larger sample size here we can actually kind of break these apart by salary tiers now in our first data set we saw that the salary careers had a higher percentage of skills in SQL and Python and here in this state as we see as we move up in terms of the salary tiers you can see that SQL and python become more important the difference here is that traditional GIS tools tend to move down in importance As you move up the salary chain the other important part is that open source cloud and data engineering skills tend to rise in importance As you move from tier to tier what's great is that these data points help validate some assumptions we might have around modern GIS with more open interoperable languages and tools being really important to the modernization of geospatial and the modernization of GIS as you can see here at the top of the list data engineering is one of the top roles with the highest salaries from this larger data set now looking at this data point this also provides some information that these engineering skills and programming skills are going to be really important and the more skills you have to work with larger data sets and manipulate data are really going to help you and be beneficial in your career progression with this set I think there's an interesting Trend about the ability to use larger data sets and how that correlates to a larger salary and your ability to move between Career tiers so what does all this data tell us why is this important and why does this matter I think there's five conclusions that I was able to draw from doing this analysis the first I mentioned earlier is that there are a lot of different roles and titles in the gis and geospatial space it makes it really difficult to tell what career path you should take what you should be learning and ultimately what you should do with your career the second is transparency the understanding which skills map to which roles wrap the map to which salaries and understanding how that interplays with your education is really important this will really help people plan their careers grow and understand how they can expand in their geospatial career path speaking of skills I think this is also has important implications for how we teach modern GIS as we can see here SQL Python and other tools like Cloud systems and data engineering tools are becoming increasingly important not just in the data space but also the geospatial space now the traditional GIS education setting these programming languages and tools are often taught in higher level classes even in a master's program if these are some of the skills that are becoming important drawing higher salaries and in demand in the job market it makes sense to start to teach these in earlier levels and as fundamental skills for a modern JIS education the fourth point is that in a job search it often helps to understand what you need to be looking for where you should be looking and how you should search for these different terms having this information incredibly excess possible is really important for anyone searching through jobs and employers as well this only helps match people together faster and helps people get into roles and Performing and adding value of geospatial much much faster the final point that I would say here is that adding specializations to your tool set is going to be really important as you look at what these different rules entail adding some specialization whether that be a language a different process that you do or something different that differentiates you from the crowd is really important even if you have some base skills adding one new language like SQL or Python and then performing some analysis and that can really bring some value and could even boost up your potential job offer in the future there's lots of different ways to use this data but ultimately I hope it's helpful to shed some light on what's happening in the geospatial career market and help you understand how you want to build your geospatial career going forward once again thank you to Looper Roost to the data that you shared with me and for putting all the code together as well so I also want to hear from you how's this information helpful have you seen this in the job market yourself and ultimately how are you going to apply this to what you're doing in your career path go ahead and share them in the comments below and I hope to hear from you soon

Channel: Matt Forrest

Views: 32,025

Keywords: gis, data science, spatial data science, spatial data analysis, geospatial, geopandas, modern gis, matt forrest, esri, arcgis, gis technology, what is gis, carto, spatial analysis, geographic information systems, gis analyst, python, sql, geospatial python, geospatial python tutorial, python gis, python course for beginners, spatial sql, postgis, bigquery, postgresql, sql tutorial, gis jobs, geospatial jobs, gis career, geographic information system, qgis

Id: pYlb34mz5XY

Length: 15min 21sec (921 seconds)

Published: Mon Apr 17 2023