Trying to decide if you'd like data analytics as a career or even as an internship? Hear from two data analysts for their inside take.
γπΌ What data analysts do day-to-day
γπ What type of people thrive?
γπ Pros
γπ Cons
γπ Impact
γβοΈ Work-life balance
γπ€ΈββοΈ Flexibility
γπ€ The people
γβ¬οΈ Your managers
γπ§ Values alignment
γπ± Learning & development
γπ Job outlook
γπ΅ Pay
γπ Career progression
γπ Exit options
4. Where can I find internships?
Honestly, every day is a bit different. Most of the time, I'm working on ongoing projects. Here's a taste of the projects I did and the tasks they required.
Projects Corresponding Tasks User drop-offs: Looking into where your product is losing people and digging into why (Is it hard to find a ride? Do they think it's too expensive?).
Funnel analysis: Let's say 1000 people sign up for Uber, but only 100 people book rides and only 10 people end up taking a ride. That's a "funnel" and you analyze it to find where users drop off.
ROI calculator: Creating a tool that shows companies how much they could save by using our product based on information like the number of employees they had and their average salary.
Data mining: Digging through lots of publicly available data. For example, I extracted information from zoominfo on companies that we could sell to.
Automated reporting: Gathering data to show which products are selling well and which aren't. This helps companies on the Instacart marketplace understand their performance compared to others.
SQL queries: Writing specific commands in SQL (a coding language for managing data) to pull out data from a large database.
At all the companies I worked at, I also did some dashboarding. This involves making charts and graphs that show trends and important data points to help people across the company make better decisions.
The important thing about dashboarding is that you have to think about what visualizations your colleagues would benefit from seeing. You can't make visualizations just for the sake of making data look pretty. There's no point in making a visualization unless it can actually be used for making decisions.
β Former data analyst @ Instacart
Here's how I break up my week.
- Monday, I look at metrics and decide what to prioritize for the week. This involves copying and pasting data from Google Analytics into tables to track where our visitors come from and what they do on our site. (This task isnβt automated yet because it requires a data engineer (someone skilled in managing large amounts of data) to set up a proper data handling system.)
- Tuesday-Wednesday, I work on what I've prioritized. Mainly, this involves keeping an eye on traffic coming from paid and unpaid sources. Paid sources would be like Google Ads. If I notice that our spending isn't translating into increased bookingsβa problem known as "diminishing returns"βI need to investigate why and tweak our strategy. Unpaid sources would be SEO (i.e. Google search) and returning customers. For example, if fewer people are revisiting our services, it might be due to the absence of a loyalty program, so I'm currently working on a proposal to address this.
- Thursday-Friday, I work on data requests from other teams.
β Data analyst @ Tablet & Michelin Hotels
- People who hate being bored and like being busy. I like having lots of work and having a lot of data to play with.
- Someone who's intellectually curious. For example, if someone asks for data ("I want to see the number of users in March who dropped off"), you could just give them a number. But it's better to be curious about the inquiries you get so you can better help them.
- People who can work on their own without direction. You're the only person who looks at the data. So you'll need to look at the data to find patterns and anomalies to find out what's going on. Maybe there's seasonality, or something's going on in the market or there are new regulations.
- People who are OK with unpredictability. Unpredictability adds a bit of spice to the role bu it's not for everyone. Some people want consistency. If you want unpredictability and a more dynamic role, it's something that'll make you happy.
β Former data analyst @ Instacart
- It's an open-ended job with a good amount of autonomy. You get to decide how you'd want to improve the company through data. You have some cyclical, mandated work that's always there (e.g. quarterly reporting), but for the most part you get to pick and choose your projects.
- It's a cross-functional role, so you get to be a part of every part of the company. If you work in accounting, you're just in accounting. Whereas with data analyst, you get to work with sales & marketing, product, engineering, and so on
- It's a good way to position yourself for future roles. As a data analyst, you learn about the company's data, which is basically a window into the whole inner workings of the company. You'll gain a great overview of the business, which will help you with future roles.
β Former data analyst @ Instacart
The main cons our data analysts brought up was the need to handle ad hoc data requests from different people in the company.
You need to be able to quickly shift gears. While doing your day-to-day work, you'll need to field random questions from other parts of the business. For example, people will make requests like "Can you take a look at the data the users from this client in X month?" or "I want to see all the users in Feb who dropped off after sign-up." So you'll need to be able to shift between different things you're working on.
β Former data analyst @ Instacart
People from other teams often ask for data that's just not important. For example, they might ask for simple things like how many people clicked on a part of our website. Because I have a view of all our data and know what matters (probably moreso than the CEO), I know that the data they're after just isn't important.
It's also annoying when people keep changing their data requests. Every time they change their request (e.g. "I want three more pieces of data"), we'd need to change our code and make sure the data is still able to load properly, which can take a lot of time.
Also, sometimes I work with external companies, like a marketing agency, and need to make sure they use the right data. It's extra work for me when they don't use the right data to make decisions on how to spend our marketing budget. As a result, I need to check their work.
β Data analyst @ Tablet & Michelin Hotels
I definitely feel like I make a huge impact. As a data analyst, you're driving company decisions.
For example:
- You'll see a change implemented on the website which fixes a bug or addresses user painpoints, and you'll know your analysis helped make that happen.
- You'll help your company win a new prospect, because you were able to show them (through data) how much money your company's service would save them.
β Former data analyst @ Instacart
I do feel like I'm making an impact. Partly because I now work at smaller companies where it's easier to push for changes.
To use data to push for change, you can't have very fancy data or build crazy models. At the end of the day, you have to tell a story, and keep it simple to persuade a decision maker. So as long as you don't have a dysfunctional decision maker (e.g. CEO), you can make a difference.
β Data analyst @ Tablet & Michelin Hotels
I work 9-5 or 9-6. There are moments when I need to cancel going to the gym after work to work on something last minute, but I'd say those moments are pretty rare.
β Former data analyst @ Instacart
Work-life balance is chill. Every week, I have to go into the office Mondays and Tuesdays. For the rest of the week, I can work from home. I can work remotely for a month every year (this applies to the whole company, not just the data team).
β Data analyst @ Tablet & Michelin Hotels
Sometimes, when there's deadlines, there's deadlines. I have weekly and monthly deadlines, but some deadlines are same day or next day. It depends on the urgency.
I might get a call towards the end of the day that goes "We need to put together a proposal for the company. We can potentially win this client. We have an opportunity that we need to seize. They want to see an estimate of their projected savings. Can you get this done by tomorrow?" so you need to switch gears and drop everything.
As a data analyst you have to make yourself available when people need you, but I'd say my schedule is mostly consistent. And as long as you communicate your schedule and are good at juggling projects, you can keep yourself flexible.
I work from home, but it really depends on the company.
β Former data analyst @ Instacart
Depending on the role, you'd either work with other data analysts or with people in other teams.
As a data analyst, you mainly use data to help people in different business functions. This means I don't really work with other data analysts, but with a broad spectrum of people and personalities.
For example:
- Once I found that a promo code wasn't working properly, so I went to the software engineers and asked, "I found this line of code. Is is supposed to be like this?"
- Another time, I worked with an accounting director to figure out why the revenue we were supposed to be generating didn't match the amount of money that was entered our bank account.
- I also worked with business development on an ROI calculator, which they'd use to help their clients calculate how much our service would help them save money.
β Former data analyst @ Instacart
I mainly work with my counterpart in product (also a data analyst). He's probably the favorite person I've ever worked with. He's as focused as me on generating profit.
We'll usually talk about the things we're working on, see what to prioritize and what to analyze. For example, we were trying to come up with a subscription plan, but before that, we had to do a breakdown of who we're trying to target.
β Data analyst @ Tablet & Michelin Hotels
At two companies I worked at, there were multiple data analysts reporting to a data manager. We'd have a weekly sprint meeting, which is a basic check-in kind of meeting where everyone goes around the table and says what they're working on. In one company, I was reporting to the CTO.
But for most of the day, you're working on your own projects, having meetings to discuss next steps as needed.
β Former data analyst @ Instacart
My manager is the head of engineering. I enjoy working with him, as I can bring up new ideas for our product and he'll tell me what's possible and what's not. It's like being a product manager.
I also feel that he appreciates my work and trusts me. I've given him reports on organic traffic and told him "These are the search terms that we need to focus on, e.g. "boutique hotels in Tokyo" and not just "hotels in Tokyo."
At some companies it's more siloed. You might work in product analytics and report to a Chief Product Officer (CPO).
β Data analyst @ Tablet & Michelin Hotels
I do feel like my work aligns with my values.
I've worked for a Medicaid-related company (Medicaid is health insurance for disadvantaged people) and a company that helps people save money on student loans.
Before I became a data analyst, I worked in insurance, just developing numbers that go into a spreadsheet. Now, I get to actually do stuff that helps people and makes a difference and I like that.
β Former data analyst @ Instacart
I really like how my work supports boutique hotels with beautiful designs. Boutique hotels are so essential to contemporary design, and I wish more people (with the resources) cared about beautiful design instead of what's "glam" or "fancy."
β Data analyst @ Tablet & Michelin Hotels
Some companies will have a development budget that you can spend on online courses and training. I was able to get reimbursed for some Coursera courses, for instance.
You learn as you go. As you're writing code or looking through code, you do a lot of Googling on the side. You're constantly developing your skills as a coder and as a data analyst.
But I feel I learned most when I worked directly with business development people and getting to see how they talked to partners and how they framed the data.
At this company, our business development team's job was to get other companies to buy our service β a platform that would help their employees save money on their student loan repayments.
Business development would be like "You have this many employees, we can save X% of money in loan repayments for them." They communicated in a way where all the fat was cut out.
It made me realize that you can have all this fancy data but it doesn't mean anything if you can't sell the product or service.
So in the beginning, you do more analysis just for analysis' sake. And as you develop more, you learn how to apply that analysis to use it for something. You learn that data doesn't matter if it's not being used for anything.
β Former data analyst @ Instacart
I'm not learning much technically. But it's fun to learn what it's like to run a business.
Also, the simpler the data the better. I realize, I don't actually have to run very fancy analysis. It's all about raw smarts and staying focused on the bottom line.
β Data analyst @ Tablet & Michelin Hotels
Many data-focused occupations are growing faster than average. In particular, demand for data scientists is growing at 35% between 2022 and 2032, according to the Bureau of Labor Statistics. (The Bureau doesn't have a separate occupation for data analysts, so we'll assume they lump them together with data scientists.)
What might explain this demand?
Job outlook is great. There's so much data that companies have and it's so competitive that any edge you can offer a company is huge. It's a great role to be in. Headhunters and recruiters reach out all the time on LinkedIn. I don't even know how I got on their list.
β Former data analyst @ Instacart
According to recruitment agency Robert Half, data analysts earn in the range of $93k-$134k.
According to recruitment agency Harnham's Data Analytics Salary Guide, here's what data analysts currently earn at different stages of their career.
| Stage | Data analyst |
| Entry-level | $70k-$123k |
| Mid-level | $90k-$163k |
| Manager | $125k-$210k |
| Director/Technical Lead | $145k-$250k |
| VP | $195k-$350k |
The highest-paying data roles are in Customer Insight or Customer Analytics.
There are two ways to progress in your career.
In tech, you don't necessarily have to manage someone to get promoted. You can get promoted if you own more projects. For instance, I pretty much oversee this marketing agency we work with, I oversee organic traffic, I proposed a new subscription plan. If you own more projects than the year before and you do well, you can get promoted.
You get promoted from Data Analyst to Senior Data Analyst to Analytics Manager. Usually it's levels-based, like L1, L2, L3, L4, L5.
I'm L2 right now. I was about to be L3 when I got laid off at my last company, so I'm at L2 at another company now, set to be promoted end of the year.
It's been hard to get promoted in tech, because there's a tech recession and companies are cutting budgets.
β Data analyst @ Tablet & Michelin Hotels
Exit options really depend on your role and your own personal preferences.
You can pivot yourself however you want. You can lean into sales & marketing or shift more into a business strategy role that's not solely dedicated to data. If you like software side of things, you could go into software engineering.
β Former data analyst @ Instacart
You can find plenty of internships on Prosple. We have a vast selection of internships curated for students like you.
And for the best tips on landing an internship, check out: How to break into data analytics as a student.