
We’re in the Middle of a Data Engineering Talent Shortage - hankmh
https://blog.stitchdata.com/new-research-were-in-the-middle-of-a-data-engineering-talent-shortage-bdd59673608c
======
jnordwick
Whenever I see these posts I immediate translate them in my head to "we're in
the middle of a talent shortage at a price I am willing to pay."

I've worked with very large amounts of data and high performance computing for
most of my career; I mostly had finance related jobs in the last decade or so.
I have most of the skill you want, including some you don't know you want.
However when salary comes up, that is where we start to part ways. If you are
really serious about a shortage, you should be really serious about making
offers that can be competitive, but I keep seeing the same $150k offers. That
isn't a "shortage" kind of offer.

~~~
stale2002
Although your statement is technically true, it is basically meaningless.

Yes, you can always always always find somebody to do a job is your a willing
to pay 10 million dollars. That means that "shortages" are impossible. It
means that you can never have a shortage in any situation, because you can
always pay 10 million dollars for a single visit to the doctor.

But this line of logic isn't very useful when talking about "shortages".

If you had to pay a million dollars for a loaf of bread, is there a shortage
of bread? IE, billions of people will starve to death by next week, because
they can't afford to buy food.

Most people would say "Yes, there is a shortage of bread".

When people talk about shortages, they are obviously talking about a shortage
at a certain price point. There is no other definition of the word shortage
that makes sense.

A good definition that I use for the term shortage is "If the government could
snap its fingers and instantly produce large amounts of X overnight, would the
world be a better place"?

If the answer is "Yes, the world would be in a much much better place", then
that means there is a shortage of X. If the answer is "No, the world would
only be a little better". Then that means that there is NOT a shortage of X.

~~~
sbov
Haven't you just done a 180? I mean, I'm pretty sure the world would be in a
much much better place if the government could snap its fingers and instantly
produce large amounts of almost anything. Therefore there is a shortage of
almost everything.

~~~
sksnxjis
Given the millions of unemployed Americans, it seems this is not true for at
least some occupations.

Wal-Mart greeters can be wonderful people and I'm not saying they aren't
valuable as humans. But in labor market terms, there is clearly not a shortage
of them.

------
mrharrison
We should rename this job position to Data Sanity Engineers.

I have been thrown these projects at work before, where I'm the frontend
engineer and I need to make some cool D3 visualization, but low behold the
data is shit, and I have to help the backend team make the data useable. It's
a mind-numbing job, that nobody wants, because it sounds like a one month task
to get a good REST API up and working, but it usually takes three months,
because you have to go back and forth making sure the data is right, and there
is always 10 tricky edge cases that you have to work some magic on. Not only
that but you need to have smart people cleaning the data, so that you don't
make some big mistake down the line or your REST API is super slow, and you
have to add another couple weeks or month to rework the data again. So that
one month becomes three months, and most likely a year, because somebody will
say that looks great but can we also add this, and it goes on and on. It's
literally a mind-numbing job that most nobody wants. I have found that
products like Tableau are the best for this, you still have to clean the data,
but it helps speed up the process.

Data cleaning is a super golden problem to solve.

~~~
dizzystar
As a contradiction to this point, some people (me) really enjoy working with
data, from cleaning, munging, creating, sorting, pipelining, etc, and find
front-end visualization production excessively boring and mind-numbing.

Give me emacs and a command line, and I have all the truth I need, which is
far more honest, in my mind, than anything that can be created with D3 or
Tableau. Beauty is in the eye of the beholder, and it doesn't really do anyone
service to look down on the work others find enjoyable. If doing D3 makes you
happy, that is awesome, and I can only congratulate you for your passion and
your ability to look forward to work I don't "get," and I wish the feelings
would be mutual.

~~~
mrharrison
So I guess you are a data engineer? What makes it fun for you? How do work
with your customers to give them what they need in a timely matter? I would be
interested to know what stack you use to go from dirty data to customer
consumption.

~~~
dizzystar
Closer to an aspiring data engineer, though I've done my fair share of ETL,
cleaning, database building / rebuilding, admin. Prior jobs have been database
engineer, probably closer to DBA.

I just enjoy working with raw data and raw code more than I enjoy writing
something that launches a graphic. I enjoy writing a script that finds a bad
piece of data, or a script that fixes up everything, or writing something that
was once unable to run at all get converted to something that runs in 500ms.
Perhaps it is that journey of constant discovery, and seeing that every
situation is a unique little puzzle. It is seeing the world _as it is_ with no
one reinterpreting what the data means for me. I can explore it and discover
what it really means. It is hollow truth, a mess of ideas converted to sets of
ideas layered on sets of ideas, and when it is finally drawn down, converted,
and passing all tests, it is self-evident and self-reflecting, and true. Hard
to explain, but I suppose I like all the things people hate about it.

The tools matter about as much as it matters what CSS framework you are using.
You have the ability to logic through UI and UX, whereas I do not. I have zero
hope of ever doing well at what you do, since I simply don't have the
foundation, but if it matters, I know most jobs I've applied to and worked at
tend to be more ad hoc, using PL, Python, Ruby, etc.

~~~
mrharrison
I'm not comparing frontend to backend. I also think data is fun and I don't
mean to be little the job, but in a real world scenario its detail intensive,
under appreciated, tons of edge cases and extremely complex if you plan to
make it scalable and fast. So if you are an aspiring data engineer be aware of
these pitfalls, because the first couple times you do it you will think its
fun to try something new and create some fun useful analytics, but customers
will often complain at how long it takes and want more. It starts to wear away
at ones drive and passion for data. Its not the data aspect its the
job/deadline aspect.

~~~
SmellTheGlove
You're getting very close to the root cause - customers and even colleagues
don't really care about the work that goes into the data. They care about the
end deliverable, because that's what creates value for them, and fairly so.
That gets at why data engineering as a discipline isn't (IMHO) very well
respected.

I know this isn't reddit, so I'll point you to reddit. Check out
/r/datascience where those folks talk about what it takes to be a data
scientist. Some folks are honest about data engineering, but most handwave
past it, or talk about it like it's beneath them. Their role would not be
possible without solid data engineering, rather than a complementary and
equally important discipline. Good luck doing "data science" or "analytics" or
"machine learning" or every other buzzword without clean data, and for us data
engineers, good luck ever demonstrating value without the analytics folks
working with us.

------
dmatthewson
From the article: "Data engineers are the janitors who keep your data clean
and flowing."

Hm, I wonder why he's having problems hiring janitors.

~~~
pavlov
Bizarrely, I remember a recent HN discussion where a poster was arguing that
any software developer who is not working in machine learning is like a
plumber.

I guess this means that the entire profession consists of janitors and
plumbers.

~~~
jballanc
Considering that plumbers and janitors have likely, in the entire history of
human civilization, done more for health and longevity than doctors and
scientists...I'm kind of ok with this analogy.

~~~
hga
Doctors, maybe, but it was the scientists who told them about the germ theory
of disease, for instance.

I've read, but not confirmed for myself, that in the US the biggest gains in
health came in the post-Civil War period, when "plumbers and janitors" made
the difference. Of course, that's really starting with, after the science, the
civil engineers who designed the public works systems that supplied clean
water and took away sewage, and let's not forget that politicians and like who
found it worthwhile to buy votes that way (now, they take our infrastructure
for granted and buy votes more directly...).

~~~
dredmorbius
Confirmation:
[https://ello.co/dredmorbius/post/MsQfdPAn_0XUdZUoReBfbg](https://ello.co/dredmorbius/post/MsQfdPAn_0XUdZUoReBfbg)

~~~
hga
Thanks! There was a delay after the Civil War as you'd expect from all the
chaos and disruption that caused (e.g. MIT got its charter before the
outbreak, but wasn't able to start up until after), but it's pretty clear, and
gets really dramatic the further you go forward.

~~~
dredmorbius
I've been studying the period (mostly the Industrial Revolution and onward,
though the accelleration of the late 19th / early 20th century is staggering),
and it's pretty phenomenal.

There was a lot going on. Germ theory, of course, was part of it. But public
health measures, especially sewerage systems, clean drinking water, and
municipal waste removal, were all massive contributors. Note that the decline
in mortality occurs _well in advance of_ antibiotics and even most
vaccinations.

For all the recent debate on vaccinations, it's interesting to note that the
peak period of their impace (roughly 1930 - 1960) saw relatively little
reduction in _mortality_ , though there was a _large_ decrease in disease
_incidence_. It turns out that with septic control, antibiotics, food quality,
and nutrition, many viral diseases weren't killers, but _did_ present quality-
of-life issues. And yes, often quite severe -- polio was no joke, and I know
people who've suffered lameness from it myself. Measles and smallpox are
similarly scarring and have long-term impacts.

But the major impacts of virtually _all_ medicine are front-loaded to the
period _before_ 1950, with much the gains _since_ attributable to either
greater access (especially for the disadvantaged) and removal of environmental
agonists (lead, tobacco, alcohol, asbestos, miscellaneous poisons, safety
hazards).

------
tom_b
Ignoring the breathless nature of the article, this is a buzzword label for a
commodity skill set that pays a commodity salary in tech. It is also the
commodity skill set that my employers have all paid me for.

There has been for a _long_ time hype around new technology and labels for
business intelligence, data warehousing, big data, and now data
engineering/science. I'm not saying there are not some roles in this space
that return huge value to organizations, but that these opportunities are much
rarer than the buzz indicates.

I wonder if the perceived shortage is mainly hype as the shift to new cloud
technologies makes many of the older ideas a little less useful - if you are
plowing data into BigQuery, you probably aren't so worried about your star
schema data model for reporting.

I would strongly advise people that look at these types of articles to look at
the roles in question and ask "Is this role on the critical path to customers
paying us?" My experience has been that the answer is often "No." This is bad.
I have also seen situations where businesses that do rely on smart data
integration can show that they are selling dollar bills for ten cents that
_still_ have trouble getting customers on board with spending that ten cents.
Business is weird.

------
mattnewton
I'm trying to switch careers into "Data Engineering" now, as a full stack
developer who is more interested in ML, and I've found almost no traction
internally at my company or externally. It looks like I may just accept a full
stack position at a good company that does a lot of data science for now, but
though I would ask - Where are all these jobs?

~~~
achompas
My official title is "Data Scientist" although I'm closer to the "ML Engineer"
someone else mentions in a child comment.

Frankly speaking, if your company doesn't need a data engineer, it won't hire
one or move you into that role. They likely don't, either, if you're
experiencing this pushback -- data engineers often develop ETL pipelines or
data warehouses, both of which are very useful if your company has a data team
and very useless if it does not.

That said, you may want to move closer to my role. There's actually a shortage
of data-savvy people who can also write production software, and you would
nicely complement a more research-inclined data scientist or analyst --
someone with far more experience with research/analysis than development.

~~~
p4wnc6
> There's actually a shortage of data-savvy people who can also write
> production software, and you would nicely complement a more research-
> inclined data scientist or analyst -- someone with far more experience with
> research/analysis than development.

I experience the same problem with shortage-at-price-X in the field you
describe. I'm a machine learning engineer with experience in MCMC methods, but
I also have a lot of low-level Python and Cython experience, some intermediate
experience with database internals, and lots of experience writing well-
crafted code for production systems.

There are basically zero companies willing to pay what I'm seeking (which is a
salary based on my previous job and a few offers I got around the time I took
that job). In fact, in some of the more expensive cities, the real wage
offered is far _lower_ than other markets.

I've seen reputable, multi-billion dollar companies offering in the $140k
range for this type of role in New York. That's wildly below anything
reasonable for this sort of thing in New York. I've seen companies in
Minneapolis offering $130k for the same kind of job -- and even _that_ is
still too low for Minneapolis! The same has been true in San Francisco as
well.

Because these companies value you more for simply looking good on paper and
looking good as a piece of office ornamentation when investors stroll through,
and they view you as an arbitrary work receptacle closer to a software janitor
than a statistical specialist, their whole mindset is about how to drive wage
down.

Frankly, given the stresses of the job and the risk of burnout, I think it's
actually a terrible time to be in the machine learning / computational stats
employment field, despite all of the interesting new work and advances being
made. The intellectual side is good, but the quality of jobs is through the
floor.

~~~
geebee
"I've seen reputable, multi-billion dollar companies offering in the $140k
range for this type of role in New York. That's wildly below anything
reasonable for this sort of thing [in NY/SF"]

Man, do I ever agree. This is where the "shortage" argument falls apart.

This is why I'm so uninterested in the abstract arguments happening elsewhere
on this topic about whether markets are failing and basic laws of supply and
demand no longer apply at theoretical salary levels (10 million was offered as
an example).

Why are we bothering with this debate, when it's so far from reality? I'd say
that if you're trying to hire a very high skilled and critical tech worker in
SF, and you just can't find one no matter how hard you try, and then I find
out that you're only offering 140k a year?

In San Francisco and New York (and anywhere else in the US, really), that's
nowhere close to the kind of pay where we should start scratching our heads
about a shortage and start wondering why the usual laws of supply and demand
aren't working anymore.

------
ef5a0b0628
Every time something comes up on HN about a talent shortage in a field related
to software engineering, it hurts. I have been unsuccessfully looking for a
full time position since my last start up (I was not a founder) folded six
months ago. I have been on over 25 in person interviews and gone through
untold degrading whiteboard interviews, code tests, trick questions, and take
home projects; all have ended in rejection. This industry has a need to
torture candidates because we are all considered to be liars by default. Much
is said about combating impostor syndrome in ourselves but we are too eager to
engender it in others.

It seems people in this industry refuse to understand that some people are not
perfect. I never graduated college because I hated it with the very fiber of
my being, so I am not particularly great at white boarding answers to
algorithm questions off the top of my head in a high pressure environment. If
I need them during my job, I look up answers and learn from people who are
much smarter than I am.

My personal identity has been shattered, as I thought my ~5-10 year history of
success in the industry indicated I was in demand and talented. I saw posts
like this and thought that if the worst happened I'd still be able to find a
job. The idea that there is a talent shortage is a lie, or candidates like me
wouldn't be treated as I have been. I'm not asking for a free job, or a
handout. I have had a successful career so far and am capable of doing good
work. But I'm not a specialist in Big Data Machine Learning Neural Networks.

I have struggled with bipolar disorder and suicidal ideation most of my life.
I've dealt with the death of my beloved grandmother and my father who was
instrumental in my choosing to be an engineer with only minor lapses in
control. Nothing has caused me to consider taking my own life as much as the
past 6 months. It seems there is no future for me in the only career I have
any skill in and which is a huge part of my identity. And to constantly be
told that there is such a shortage of engineers only salts the wound.

~~~
googletazer
" I have been on over 25 in person interviews and gone through untold
degrading whiteboard interviews, code tests, trick questions, and take home
projects; all have ended in rejection."

The fact that you pulled through 25 of them is already commendable.
Unfortunately as a labor provider you'll be subjected to all kinds of crap for
the privilege of working.

Every single person on here needs to have a secondary business going on right
now. Doesn't have to be a highly skilled industry either, selling hand made
stuff on Etsy can be a lifeline in these situations.

------
rch
I've heard more than one CTO/Sr. Engineer refer to people in these roles as
'data grunts' or something similarly dismissive. Then they're mystified as to
why solid engineers are so quick to move up or out, year after year.

------
skynetv2
anything and everything is marketed as "data science" and "data engineering"
these days becasue this is the buzzword of the day.

I've been dealing with large data even before "big data" was a word but i dont
call myself "data scientist" or "data engineer". I am still a software
engineer working on what benefits my organization.

"Serial Entrepreneur" is the same these days, claimed by anyone who had a
lemonade stand as a kid.

~~~
Swizec
> I am still a software engineer working on what benefits my organization

But if you saw a nearby local maximum that's higher than your current local
maximum, wouldn't you change what you call yourself, if it means being paid
more but doing the same work?

This is similar to how the average "software engineer" makes about $30k/year
more than the average "programmer".

------
jboggan
It's digital Charlie Work [0], that's why.

I really enjoy that kind of work but it is difficult to articulate your
business value in that environment. The best thing is working closely with a
data scientist/front-end dev who can deliver products to the analysts and
executives that need the data and make sure that you get the credit for
enabling new streams of data. But most of the time you are putting out someone
else's dumpster fire.

One advantage of data engineering: unlike front-end work, there are few non-
technical people who will have an opinion on how you are doing things and
burden you with bikeshedding.

[0] - [http://www.avclub.com/tvclub/its-always-sunny-
philadelphia-c...](http://www.avclub.com/tvclub/its-always-sunny-philadelphia-
charlie-work-214628)

------
GeneralMayhem
There are 6600 jobs listed and 6500 individuals on LinkedIn with that
particular title, and therefore there's a shortage? Seriously?

* How many aren't on LinkedIn?

* Since the whole article is about how the job title is poorly defined and growing in prevalence, why would you assume that people who don't already have such a job would use the term?

* The "growth" charts on the full study are just as bad - how much of that is just from renaming existing generic developer positions, since "data engineer" is clearly a relatively new term?

~~~
sportanova
6500 data engineers on all of Linkedin, but 6600 job openings in the bay area.
so there are more job openings in one area than all data engineers on linkedin

------
binalpatel
The fact that the original, unmodified article referred to data engineers as
"janitors" pretty much says it all.

It's very analogous to front-office and back-office work in Investment
Banking. "Data Scientist" are the front-office, with all the prestige, and
"Data Engineers" are the back-office, doing a lot of the heavy lifting without
nearly as much recognition.

In my opinion there shouldn't be a delineation. You shouldn't be a data
scientist if you can't gather, process, and clean up your own data.

~~~
biztos
Ideally you'd have a symbiosis, and each side would recognize the importance
of the other.

Even if you require your data scientists to be able to do engineering work,
it's probably way more efficient to have some good generalist Software
Engineers doing all the "pre-math" work and freeing your statisticians up for
what they're (hopefully) good at.

Plus as a side effect, your software will probably be better.

------
ThePhysicist
Data engineering sounds much better than "data plumbing", but in my experience
the latter is a more accurate description of the work of a data engineer:
Building -and often unclogging- pipes that transport data from A to B, and
putting in filters to clean it and extract the useful bits.

So why not change your LinkedIn job title to "data plumber", which is sure to
get you some serious recruiter attention ;)

------
untilHellbanned
Ahh the ol' write a post about a not well understood distinction and then
proceed to not explain the distinction.

Looks like we need more English engineers too.

------
cutler
I'm puzzled at the omission of Scala and Spark in this report.

------
protomyth
I worked for about 10 years doing exactly what they want, but I ended up
having to write a lot of the tools which means I'm not able to check the boxes
on some tool you require which gets me punted by HR.

I'm starting to think that the message is if HR is going to do checklists then
developers should really make sure they work mostly with contracts that use
popular checklist items.

~~~
mulmen
As a data person I would really like to put some numbers on how much the
typical HR hiring process costs a business. I don't know anybody that says
they are happy with how hiring works in he tech industry but I've also never
seen an HR person try and improve the process.

~~~
pyb
That's because the system is already optimised for the needs of HR people.

------
makmanalp
Quick sidenote, anyone know where the databases / distributed systems
engineering jobs are at? E.g. if one wanted to not use these tools but also go
help build these tools?

I can think of Facebook, Google, Microsoft, IBM (which locations and groups
within these companies / where?). I can also think of Confluent, CitusDB,
Databricks, etc.

~~~
rhizome
Market Research is a $40B industry that depends almost completely on these
concepts. I'm not sure how prevalent distributed systems are with MR
companies, but that's an implementation detail anyway.

~~~
serge2k
> that's an implementation detail anyway.

Which is what the poster was asking for.

------
lifeisstillgood
Weirdly the problem is most hires have it backwards.

Before going out to the market and discovering what talent exists and
consequently what salary it will take to get them to join (ie negotiate)
_most_ organisations decide on a salary range, usually reflecting the current
internal structure not the current external market.

The longer an organisation has existed the more out of whack with the market
its internal set up is.

As such companies decide on their price point first, then go looking. Which is
of course backwards.

------
otto_ortega
Am I the only one who thinks there will be a ton of people changing their job
title on LinkedIn to "Data Engineer" as a result of this article?

~~~
collyw
I am thinking about it. Actually a friend recommended that I change my title
to Data Engineer a few months back.

------
realworldview
We surely need data mechanics.

------
slantedview
These "shortage" stories always make me roll my eyes, because they're usually
about money more than anything. And money is usually about cost of living more
than anything.

If you choose to locate your company in one of the highest cost of living
regions in the world, then you are complicit in the "shortage". Supply and
demand - pay up. Or don't.

------
moandcompany
I am a data engineer working on a machine learning team with models actively
used as part of our product(s).

From my experiences working in various contexts (applied machine learning,
analytics, policy research, academics, etc...), there are several of factors
that contribute to this shortage: (1) "data engineering" often requires a lot
of breadth and knowledge, (2) "data engineering" is often (derisively and
naively) referred to as the "janitorial work" of data science, (3) the
spectrum of roles and requirements within the "data engineering" domain, in
terms of job descriptions, can range from database systems administration, to
ETL, to data warehousing, curation of data services / APIs, business
intelligence, to the design/deployment/operation of pipelines and distributed
data processing and storage systems (these aren't mutually exclusive, but
often job descriptions fall into one of these stovepipes).

Some of my quick thoughts and anecdata:

Companies have made large investments in creating 'data science' teams, and
many of those companies have trouble realizing value from those investments.

A part of this stems from investments and teams with no tangible vision of how
that team will generate value. And there are several other contributing
factors…

"Dirty work." People haven't learned how to, and more often don't want to do
it. There's a vast number of tutorials and boot camps out there that teach
newcomers how to "learn data science" with clean datasets -- this is ideal for
learning those basics, but the real world usually does not have clean or ideal
datasets -- the dataset may not even exist -- and there are a number of non-
ideal constraints.

There are people that wish to call themselves “data scientists” that “don’t
want to write code” and would “prefer to do the analysis and storytelling”

Engineering as the application of science with real world constraints: there
are a number of factors that we take into account, often acquired through
painful experience, that aren’t part of these tutorials, bootcamps, or
academic environments.

Many “data scientists” I’ve met have a hard time adapting to and working with
these constraints (e.g. we believe that the application of data science would
solve/address __ problem, but: how do we know and show that it works and is
useful? what are the dependencies, and costs of developing and applying that
solution? is it a one-time solution, or is it going to be a recurring
application? does the solution require people? who will use it? what are the
assumptions or expectations of those operators and users? is it suitable? is
it maintainable? is it sustainable? how long will it take? what are the risks
involved and how do we manage them? is it re-usable, and can we amortize its
costs over time? is it worth doing? This is part of a methodology that comes
from experience, versus what is taught in data science)

Larger teams with more people/financial/political resources can specialize and
take advantage of these divisions of labor, which helps recognize the process
aspects of applying data science and address some of the above

Short story: if you view data engineering as "janitorial work" you're missing
the big picture

Anyone else notice that the attributes of a 'unicorn' data scientist include
the traits of a 'data engineer?'

~~~
vijayr
How does one get started with this? I suppose a lot of people who hang out at
HN are competent devs good in programming and databases, but probably
beginners in math, ML, AI etc. How does such a person get started and find a
job in this field?

------
cheriot
It was only 20 years ago that companies hired a "web master" or a generalist
to do everything. But pieces of those jobs became specialized. Now we need UX,
UI programmer, general engineers, dev ops, data engineers, a data scientist,
etc.

And how many companies are still interviewing with fizzbuzz?

------
collyw
So I know SQL, Python, Django, Java (though its been a while), Javascrit,
Linux, some cloud computing and a bit of devops. Am I a data engineer?
Software engineer, with a lot of database background? What makes a data
engineer different from a software engineer?

~~~
njd
\- The challenge for an organization is to recognize that there is a
significant difference between the 'data engineer' working on a vertical
project and the 'data engineer' responsible for integrating data across the
enterprise.

\- The project 'data engineer', in today's world, most likely will be a
software developer responsible for ETL, etc. The data design will be more or
less up to the software developer.

\- An enterprise 'data engineer' is more concerned with data that affects the
enterprise. This typically involves some sort of data integration. For
example, how to integrate relevant data from N projects (e.g. A,B,C .. Z)
where each project has its own idea of how to represent similar concepts (e.g.
person, user, customer), with different provenance, truth assertions, access
rules, data retention periods, granularity of metadata (e.g. at the attribute
level vs entity level), etc. The enterprise is interested in questions like
'What did we know and when did we know it?", etc. The enterprise 'data
engineer' will probably levy requirements on the project 'data engineer' to
meet the enterprise's needs.

------
LawrenceHecht
Just checked, the # of data engineers rose to 9,246 (42%) in the last six
months. So, the shortage is at least being addressed by people changing their
job titles on LinkedIn.

------
wpiel
What I've learned from the comments: If something is valuable, there is a
shortage of it.

I'm not even sure if I'm being sarcastic.

------
edoceo
We hire only the best! We only hire the top 1% of candidates.

But only 1 out of 100 are qualified :(

