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What I actually do as a Data Analyst in 2025 | Roles, Salaries and AI Impact | Ingrid Emilia | YouTubeToText
YouTube Transcript: What I actually do as a Data Analyst in 2025 | Roles, Salaries and AI Impact
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So, you're considering a career in data
analytics, but have little idea of what
your day-to-day would look like and
whether you're going to be replaced by
AI? Well, this might be the video for
you. If you're new here, hi, I'm Ingret.
I share my career journey and life
online. And for the last 7 years, I have
been working as a data analyst in
London. Since this career path is
evolving every year, I wanted to chat
through some of the data analytics roles
still available in 2025 and what I
personally do as a lead data analyst.
We're also going to graze over the
salaries and the scary AI taking away
The role at its core is to use data to
solve business problems. You do that by
talking to your stakeholders to
understand the real problems that
they're facing and real questions that
they're trying to get answers to. You
then identify the types of data that
you're going to need to answer those
questions. where is it stored and
whether it's even reliable for you to
use. You then code 90% of the time in
SQL, sometimes in Python to clean, join,
aggregate, and manipulate those
different data sets. Next, you analyze
the data to spot trends and patterns.
And after all of this analysis, you
visualize the most important insights or
metrics through data visualizations,
dashboards, reports, or slide decks. And
at the end of it all, you present your
findings and recommendations to your
stakeholders who asked you that question
in the first place. What I would love
for you to take away from all of this is
that the key here is not the coding.
It's to be able to connect the numbers
and the trends and the patterns that
you're seeing back to the problem that
your stakeholders are facing and help
them solve that problem through data.
That's what differentiates a query
monkey from a really good
businessoriented data analyst. Remember,
coding is just a tool to help you answer
One of the most common data analytics
roles is a hybrid role between building
automated dashboards and interactive
reports as well as answering ad hoc
business questions. That role might just
be called a data analyst. You might be
called a business analyst. And if it's a
lot more reporting and dashboard
focused, you might be called a business
intelligence analyst. As this type of
data analyst, you're basically helping
the business to make sense of all of the
data that is available to them. So some
of the role will involve building
interactive dashboards and automating
some of the reports so that people in
the business can selfserve the data in
an easy interactive and visual way.
However, it's not just about pulling the
data and building a dashboard. It's
talking to your stakeholders to define
the requirements for the dashboard. You
agree how you're going to define the
metrics such as an active user or a
churned user. A will also involve making
sure that people in the business have a
shared understanding of that. And then
in addition, you're also likely to get
some ad hoc requests. Any sort of
question that your business stakeholders
has, such as, "We've launched a referral
program. Which city did it perform best
in?" And this is your opportunity to dig
a little bit deeper into the data on
some specific business question and then
create a collection of data
visualizations which will tell a story
to answer that question that you are
being asked by your business
stakeholders. This role in most of the
data analytics roles, you will be
working with SQL every single day. You
will be using it to clean, manipulate,
aggregate the data set. You might be
creating new data sources and ensuring
data quality. This role is a very
generic data analytics role. It is a
great starting role for any data analyst
as it helps you build really good
foundations in data analysis, in data
visualization and reporting and you get
introduced in how to use data in solving
Another common branch of data analytics
is data insights analyst. And if the
role of a previously discussed data
analyst is to build out reporting and
the metrics to help business understand
its performance, the role of an insights
analyst is to take those metrics one
step further and understanding what
drives the change in the metric or user
behavior. Now, this is quite a different
role because it relies more heavily on
having good business acumen, problem
solving, storytelling, and presentation
skills. And we're going to deep dive
into that in just a second because
that's a role that I specialize in.
Another area which is on the rise is
product analytics. The product analyst
almost combines the role of the two
discussed previously. However, they are
embedded to a specific product. And when
I say product, I don't mean the physical
product. I mean a digital experience or
a service that a company offers. Could
be an entire website or just a checkout
flow or a mobile apps landing page or an
onboarding flow for new users. They're
also embedded in a cross functional
product squad and that's a bunch of
people with different skills trying to
build that experience and bring it to
life. Because of that setup, problem
solving, product thinking, communication
are extremely key parts of a product
analyst role and you will be responsible
for measuring and understanding the
product performance, user behavior and
giving recommendations on how to improve
that experience. Now, in some companies,
it gets a bit more technical and some of
the product analysts are responsible for
experimentation. However, that part of
the role is a little bit more technical
and in my opinion overlaps with product
data science and that's a field that I
want to ultimately get into. So, okay,
Ingred, but what do you do as a data
I actually do quite a lot of things as a
normal data analyst. I don't really do
reporting and I don't build operational
dashboards. I work on quite big
open-ended questions which require a lot
more time and dedication and depth in
order to answer them. The way that I
would describe what I spend a lot of
time doing is I obsess about the why.
Why is our user base growing? Why is our
user base turnurning? Why have we been
able to sell more products to this type
of customer? And what can we do about
all this? Most of my insights work comes
from quarterly planning sessions when we
meet with business stakeholders. My
stakeholders are growth, marketing,
product, and some operations. We would
then go through what are the key product
launches, what kind of growth are we
trying to drive, and where do we feel
like we need data support? And this is
where the projects that I'm working on
get identified. Because my projects tend
to be quite big and open-ended, it's up
to me to decide how I approach it. It's
really important to have hypothesis or
clear questions in order to guide that
analysis so you don't get lost in all of
the different avenues that you could
take. After that I basically live in SQL
and Python and go deep deep deep deep
into the data. Every two weeks I try to
share something with my stakeholders.
Sharing insights is important because
that sparks discussion conversation and
that can help me guide formulate more
hypothesis off the back of the
discussions that I have. An output of
most of my work is some sort of slide
deck and I have produced so many in my
lifetime and I can truly say that I
spend equally as much time on the coding
as I do on creating a story through
slides and data visualizations. I think
a lot of analysts underestimate the time
that it takes to actually pull the
insights and turn it into something
meaningful that your stakeholders will
understand. And if you would like to
hear more about how to structure a good
PowerPoint presentation with your
analysis, let me know. It's definitely
something that I'm quite good at and
something that I'm passionate about so I
can definitely share in a future video.
I also never just send a slide deck in
an email or a Slack message. I always
schedule a presentation cuz that creates
an opportunity to have a discussion to
ask questions but also to make sure that
everyone is taking away what you want
them to take away because ultimately
it's your analysis. you know what you
have found. You want everyone to be
aligned on that story. It can also help
you get feedback on where your
interpretation isn't quite right. Your
stakeholders will always know more about
the product or the area and what is
happening which might help explain why
you're seeing your data in the way that
you are seeing it. I hope this brings it
to life a little bit. Yes, I quote a lot
and I live in SQL and sometimes Python.
However, I spend so much time
visualizing the data, thinking about the
why, and also presenting and
communicating with my stakeholders. So
those soft skills are equally if not
more important than the technical
skills. I think a more insights focused
role is a really good way to transition
from analytics towards data science
because in order to pick up those more
nuanced user behaviors, you can apply
regression, clustering or other machine
learning techniques to spot that. So you
while you will not be expected to build
productionready data science models, you
will get the opportunity to use real
world data on a real world problem that
your business is trying to solve and
that's an extremely valuable addition to
your CV as an aspiring data scientist.
Let's talk about compensation. Bear in
mind that I live in London in the UK and
this is based on my research as well as
my experience when recruiting for data
analytics roles last year as well as
this year. So for a graduate job or an
entry-level job, I would expect the pay
to be between £25,000 a year to £35,000
a year. For a mid-level with maybe 2 3
years of experience, I would expect the
pay to be between 40,000 to £50,000 a
year. For a senior that's talking maybe
four to six years of experience 50 to75,000
to75,000
and for a lead data analyst with maybe
five plus years of experience 75 to 95k
a year goes without saying that these
are very industry sector and company
dependent. If you work in public sector
such as NHS or government jobs or maybe
some of the media companies you will
probably get the lower end of the
bracket. If you work in private sector
such as banking, insurance, consulting,
obviously tech, you will get on the
higher end or some companies you know
like the big tech, they will pay above
the bracket. These are also based on you
being an individual contributor rather
than a team manager. With team
management comes more responsibility,
different type of skill set and you do
get paid more when you manage a team.
You would have also noticed that I
didn't put a six figure salary and that
is because I actually do believe that
it's quite hard to hit six figures in
data analytics in the UK and in London.
That is not to say that those roles
don't exist, but I think you will have
to be quite experienced maybe on the
lead/staff level and you will be
required to know some data science, data
engineering in order to land a six
figure salary. I generally found that
product analytics roles tend to be
higher paid, but I think it's because
you have to be quite a well-rounded
business orientated data analyst and
some of them do require a little bit of
data science experimentation knowledge,
which makes sense why they would pay a
bit more. But that is not to say that
there isn't growth or opportunity. I
think there's a very clear progression
if you continue to upskill and keep
yourself up to date with the latest
developments in the field.
I'm sure some of us have thought that AI
is an end of many careers as well as
data analytics. It is very true that we
definitely don't need as many data
analysts as before. We also don't need
as many analysts pulling basic queries.
I can already see some of my product
stakeholders writing code with AI rather
than waiting for the data team to come
back to them. But the reality is real
world data is always messy, sometimes
incomplete, and always needs context.
You need to know what data source to
trust, what is out ofd, and how it
connects to what's happening in the
business. All of that needs a human.
Most companies are also not startups.
They're very old and big and their data
systems are quite complicated and some
of them are regulated and it's going to
take quite a while for them to rebuild
their data infrastructure and their data
sources to be accurate enough for anyone
to write a couple of questions and
expect that the answer that it's giving
is using correct data. And that's a risk
that a lot of businesses are unwilling
to take. Instead, what AI is doing is
making analysts, engineers, or data
scientists a lot more efficient in what
they do. Now, I don't spend hours on
troubleshooting or correcting syntax
which doesn't work. I can do that with
IGBT. I can also spin up an ML model in
minutes. However, what I put into that
model needs to be created by me. Using
correct metrics and making sure that I'm
looking at the right data is more
important than the model itself. And
more importantly, my value as a data
analyst isn't coding. It's knowing the
business inside out. It's knowing what
metrics really matter and spotting when
numbers are off because of a weird
product launch that we had two years
ago. AI is not going to know any of it.
So, no, I don't see data analytics
disappearing. I see it evolving. AI is
making my job more efficient so that I
spend less time troubleshooting and more
time crafting impactful business
stories. And by focusing on turning data
into strategy and becoming a thought
partner to the business, you are
futureproofing yourself as well. And on
that lovely note, the sun has set and I
will wrap up here. Thank you so much for
sticking till the end if you did. Feel
free to drop me questions in the
comments or subscribe and follow for
more of this. Bye. [Music]
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