Data Analyst Job Seekers

How do you become a Data Analyst?

The first thing to say is that becoming a Data Analyst is very different from becoming, say, a lawyer, engineer, accountant, or doctor. There is not a single formally recognized and required qualification needed to ‘practice’ data analysis. In this sense, it’s quite like most other modern roles in tech, like software engineering, growth marketing, design, product management, and DevOps.

Side Projects

Doing your own side analytics projects is a great way to learn new skills, and get experience even if you don’t have any formal experience. It’s also a good opportunity to see if you actually like analytics or not. You’re only limited by your imagination with these projects, but here’s a quick project idea. Obtain your own data from Spotify, LinkedIn, Facebook, etc., and set yourself a challenge.

For example, maybe for Spotify, you can imagine you work there as a Data Analyst. What might be some important questions to answer, like ‘How much time do I listen to music per week? Is there a day of the week when I listen more? When during the day do I listen? How have my listening habits changed through time?’. You can download the data, and use an analytics tool of your choice to do the analysis and create some visualizations. Finish up by challenging your presentation skills - explain your findings to your parents or partner, and see if they understand what you found.

Data Is Everywhere

‘Every role is becoming a Data Analyst’, says Google executive Ben Jarvis. It’s so true, that every role, in every function, in every organization is increasingly becoming a data role, because of the explosion in the generation of data and the realization that making data-informed decisions makes companies more productive. This means that really anyone in any role should have access to some reasonable datasets. What can you find available in your role to help you make better decisions?

Even if you don’t have much opportunity to do significant analytics, perhaps you can still upskill in some of the fundamental skills. For example, maybe there’s a manual XL report that you have to create - could you automate that with SQL instead?

Almost Analytics

If you’re in a totally unrelated field, is there a way you can worm your way across to a ‘nearly’ Data Analyst role as a transition step? For example, some financial accounting roles typically include a core reporting & basic analysis component, even if they’re not thought of as analyst roles. Try to think of the role in terms of the required skills needed, and find any role where there is a crossover with the skills required to be a Data Analyst.

MOOCs

Massive Open Online Courses (MOOCs) like Udemy, Udacity, Coursera all offer data analytics courses and a tiny fraction of the cost of a formal university degree. These are a great way to cover the basics, in your own time at home. The biggest mistake people make with MOOCs is to not actually try and implement what they learn. You have to apply your knowledge, otherwise, it will go in one ear and out the other. With some newly learned skills from MOOCs, why not try a side project or put your hand up for a new project at work?

Here’s a great aggregation of the data science courses.

Formal Qualifications

In about 2018, universities started launching data analytics and data science degrees. The first graduates of these courses started hitting the market in 2020, so it’s still fairly early days for where these courses lead and the value of them. It is still very rare, for example, to see ‘data analyst degree’ as a requirement in Data Analyst roles.

How much money does a Data Analyst make?

Data Analyst salaries vary considerably by seniority, market, and industry.

For Australia, as an example, junior data analyst roles normally make a base salary of around $70 000-$90 000, mid-level analysts (say, 2 years experience) are currently $90 000-$120 000, with senior data analysts on $120 000-$160 000.

For candidates, prior to engaging with a hiring process, we’d recommend understanding the current market values for your skills. You can do this fairly easily by getting a sample of data analyst roles in the same country as the role you’ve applied for. This is a great chance to practice your analytics skills and present your findings during negotiations.

LinkedIn, Indeed, Monster, Seek, etc. - depending on where you are in the world - are good sites to get some current salary data. Of course, most companies choose to omit the salaries from their job ads, which is a shame, given it helps to promote pay equity. Transparency - including pay transparency - is a core principle of ethical hiring. You can find Alooba’s Ethical Hiring Guide here.

How can I find out about current Data Analyst positions?

Check out the Alooba Jobs board for current data-related openings from around the world. Additionally, keep an eye on your favorite companies’ career pages and other major jobs boards like LinkedIn & Seek.

What is a typical Data Analyst hiring process?

If companies don’t use Alooba in their Data Analyst hiring process, the process is typically as follows:

1. Apply for the role, uploading your CV. Your CV is then manually screened by a recruiter or talent acquisition manager. They will, roughly speaking, be looking to see how closely your profile matches the job requirements. Of course, this can be subjective, and with all your personal information on a CV (name, gender, ethnicity, age, etc.) it’s open to bias.
2. Shortlisted applicants are then phone-screened with a short 15-30 interview. The phone screen is typically non-technical, as it’s conducted by someone in talent acquisition or a recruiter, who is not normally well-placed to understand your skills. They’ll ask more behavioral questions and ‘cultural fit’ questions, that tend to be more subjective. Make sure you have lots of prepared answers to demonstrate your alignment with the organization's values and also common soft skills (e.g. leadership, attention to detail, teamwork & communication skills). They’ll also ask housekeeping questions - like location, salary expectations, current working rights, etc.
3. The hiring team will conduct a series of interviews (2-3 is normal), that will be with the hiring manager (e.g. Head of Data Analytics) and your future more senior peers (e.g. Senior Data Analyst).
4. Take-home test. Hiring managers will often cobble together their own datasets and create a take-home test. This article describes take-home tests (case studies) in more detail.
5. Final interview. Normally if you have completed a take-home test, you will then discuss it in more detail in an interview. You might need to elaborate on your findings and they’ll be looking to understand more about your thinking, and hopefully should provide some feedback.
6. Some background & reference checks are conducted. The organization might do a police check, and ask some of your former employers about you. You should make sure you have these ready to go before the organization asks for them, as otherwise, you’ll be scrambling around, which will only delay you receiving an offer.
7. Offers made, and hopefully, signed! Congrats, you’ve landed a Data Analyst role!

If companies use Alooba, then the process will sometimes be a bit different:

1. Apply for the role, typically no need for a CV. You’ll then be invited to a short skills assessment, designed specifically for the role. This helps to reduce the bias inherent in manual CV screening.
2. Candidates who score well on the skills test automatically get an interview - get a foot in the door based on merit. The interviews will then often be structured interviews which is a great way to reduce bias and helps ensure every candidate gets the same chance. These will normally be with the hiring team with 2-3 interviews and they’ll be with the hiring manager (e.g. Head of Data Analytics) and your future more senior peers (e.g. Senior Data Analyst).
3. (Optional) in-depth assessment on Alooba. Instead of a take-home assessment which will normally take 5-15 hours to complete, Alooba customers choose an in-depth assessment of skills that’s normally much shorter (1-2 hours is normal).
4. Some background checks & reference checks, as above.
5. Offer made - congratulations!

How impacted will Data Analysts be by AI?

It’s impossible to predict the long-run impact of AI on any field, especially as AI is developing so rapidly. In the short term, the general move to be more data-driven and the exponential increase in the production of data means that there should be an increasing demand for Data Analysts.

Which organizations employ the most Data Analysts?

The biggest employers of Data Analysts are normally large enterprises in the industries that are most data mature, such as tech, banking, finance, telco, and travel. For example, the FAANG set of companies, and equivalent companies in other markets (Tencent, Alibaba, Yandex, etc.). Data Analyst roles are becoming more and more common and now most organizations above a small size will have some analytics teams.

Who is not well-suited to being a Data Analyst?

We wouldn’t like to discourage anyone from being a Data Analyst. If you currently lack the skills needed to be a Data Analyst, that’s fine - don’t worry! Your skills are not fixed - you can always learn new skills, given the right mindset.

What are the best things about being a Data Analyst?

There are lots of good things about being a Data Analyst, such as:

• Solid career prospects
• Good salary
• Good working conditions
• Remote work potential
• Engaging work

The work of a Data Analyst is highly analytical (obviously), logical work, that is also quite collaborative. You’ll need to talk to other people, understand, and learn about the key drivers of the organization you work for.

The right job is really about the right job for you. Try to find a job that suits your personality traits and interests. Don’t know your personality type? Why not try taking the Big 5 personality test on Alooba?

What are the worst things about being a Data Analyst?

The right job is relative - relative to the person and their interests. Data analytics often involves quite tedious, painstaking work, like cleansing data, updating SQL scripts, and iterating through dashboard designs. This is not going to be everyone’s cup of tea. Indeed, Data Analysts were recently voted the most as the most boring profession. Some people really wouldn’t like being Data Analysts.

Data Analyst roles often do not earn performance-related bonuses, and it’s rare that the top leadership (e.g. CEO/COO) come from an analytics background - that may change of course, as data is still relatively new.

It’s also important to consider what makes a bad Data Analyst role, as not all Data Analyst roles are the same.

The most common complaints that Data Analysts will have about their role include:

• Poor quality data: garbage in, garbage out as they say. Sometimes the data is of such poor quality, that it will frustrate Data Analysts who realize they will not be able to do their job effectively.
• Poor tooling: some organizations lack data maturity and might not have the basics set-up, such as a strong data warehouse with comprehensive ELT processes, and an existing suite of useful dashboards. This means you could be stuck in ‘Excel Hell’, which is a truly horrible place.
• Wrong culture: some organizations are not data-driven, so there might not be the demand for insights, or there is the demand but a lack of willingness to invest in the data ecosystem to ensure its quality.

What are some flags I should look out for when going for a Data Analyst role?

As a Data Analyst candidate, it’s important to keep an eye out for red flags in the hiring process. These are early warning signs that maybe the role and organization you’re applying for might not be the right fit for you.

• No salary on the job ad
• Slow response time
• BS interview questions
• Opaque hiring process
• Unclear role expectations

What kind of personalities are suited to being a Data Analyst?

There isn’t a single personality type that is ideally suited to being a Data Analyst, and there’s probably some benefit in having a variety of personalities within a team to provide a more balanced perspective.

You might like to learn more about your personality by taking the Big 5 personality quiz on Alooba.

What qualifications are needed to be a Data Analyst?

There are no formal qualifications needed to be a Data Analyst. That said, to capitalize on demand, since 2018 universities around the world have introduced data analytics and data science degrees, so more and more people working with data - especially in the junior ranks - do have formal data-related qualifications.

Some organizations may in general require ‘a degree’ to apply for a Data Analyst role, however, it’s almost never a requirement that the degree be specifically in data analytics.

More forward-thinking organizations have abandoned degree requirements in recent years, realizing that they were missing out on some amazing talent who didn’t have degrees and that requiring a degree was biased against those who couldn’t afford it. You certainly won’t find any degree requirement on Alooba job ads!

How do I improve as a Data Analyst?

The first thing is to establish a growth mindset - you have to actually want to change and improve. Then, it would be helpful to understand what the expectations are of you, and where you might be falling short. Your manager should be the one to set expectations with you and provide ongoing feedback on your performance.

Benchmarking your own skills with Alooba World will give you a quick snapshot of free insights into your strengths and weaknesses, especially in the technical skills required to be a Data Analyst.

Has your organization signed up to Alooba Growth? Alooba Growth helps organizations understand their team’s strengths and weaknesses. If your organization has an Alooba Growth subscription, then chat with your manager about taking some quizzes to get feedback & insights on your skills.

What should I include on my Data Analyst CV?

If you are applying for a role with an Alooba customer, you may not need a CV at all. Many Alooba customers have replaced biased CV screening with fair, skills-based screening. If you do need a CV to apply for a Data Analyst role, it’s important to include information that the employers will be looking for.

When submitting a CV, remember it’s a marketing document - a document marketing you. Who is the audience? Initially recruiters or talent acquisition, and then the hiring manager in analytics. These audiences are very different, with different knowledge, backgrounds and skills. Increasingly, there will likely be a 3rd audience - AI - with humans unlikely to continue being the first step in the hiring process.

If you do need to submit a CV, try and make sure that you:

• Tailor it to the relevant role requirements listed in the job ad
• Use exact match language where possible (recruiters and TAs might not know that AWS is a ‘cloud’ tool or Numpy is a Python package, so match whatever is in the job ad)
• Don’t outright lie about your history
• Omit as much information that could be used to discriminate against you (e.g. you’d want to avoid mentioning any political affiliations)
• Include metrics & quantify your accomplishments
• Don’t go into too much detail (résumé actually means ‘summary’, by the way)
• Use a standardized format & font
• Avoid business or industry-specific jargon
• Keep it to around 2 pages

It’s generally a careful balance between making it normal enough to not annoy someone who doesn’t like your font (crazy, right?), but also different enough that it will stand out from the crowd.

Always bear in mind, CV screening is wildly inaccurate, very subjective & rife with discrimination, so don’t read too much into any individual rejection. If you’ve applied for 20-30 roles and keep getting the same result, then it’s worth investigating where exactly you seem to be falling down.

What makes a good Data Analyst CV?

If you are applying for a role with an Alooba customer, you may not need a CV at all. Many Alooba customers have replaced biased CV screening with fair, skills-based screening.

Generally speaking, a good Data Analyst CV is one that’s:

• A strong match for the relevant job opening
• Truthful, but also carefully constructed to position the candidate in the best light
• Succinct (say, 2 pages-ish)
• Clear - simple language, without endless jargon (remember, it has to be easily understandable by talent teams)
• In a format that’s easy to scan and draw the main points from
• Quantifies the value of your analysis in business terms
• Explains the different tools and techniques you have used
• Describes the skills that you have

What discrimination do Data Analysts face when getting jobs?

Data Analysts face various potential discrimination during multiple steps of a traditional hiring process.

For example, most Data Analyst roles require candidates to upload a CV at the beginning of the application process. Unfortunately, a CV reveals someone’s irrelevant personal information such as name, gender, ethnicity, religion, age, and more.

A recent study from the University of Sydney, for example, really drove home how much discrimination takes place for roles like Data Analysts. The researchers applied to more than 1000 jobs with a variety of CVs and then tracked which ones got callbacks.

The CVs were split into 3 groups:

• Group 1: Anglo first & last names (e.g. David Smith)
• Group 2: Chinese first & last names (e.g. Xiaopeng Wang)
• Group 3: Anglo first name & Chinese last names (e.g. David Wang)

They applied for the roles, then measured the interview callback rate they received across the 3 groups, which was as follows:

• Group 1 Callback Rate: 12%
• Group 2 Callback Rate: 4.4%
• Group 3 Callback Rate: 8.1%

So, as you can see, the otherwise identical Chinese name CVs got only around ⅓ as many callbacks as the same CVs with Anglo names. This proves that there is active discrimination against Chinese Data Analysts, for example.

To try and avoid getting caught up in this discrimination, try applying for roles with organizations that are committed to objective hiring practices using Alooba.

How can I benchmark my own Data Analyst skills?

You can benchmark your own Data Analyst skills on Alooba World. Alooba World has a range of free practice tests covering a variety of different relevant Data Analyst skills. The tests are all 100% free and will help you benchmark yourself against your peers.