C.V. writing for Data Analysts

I’m writing something slightly different today: a guide to writing a good C.V. for people applying for Data jobs.

A C.V. — or résumé for American readers/anyone else who prefers French to Latin — is the most important thing to get right in your job applications. But sadly a lot of people aren’t ever taught to write a good one.

Most of us learnt by reading those written by our friends, or examples found online. This is a recipe for poor quality. How do you know that those people knew what they were doing? They probably did the same as you! Over time mistaken practices creep in, get repeated everywhere, and no-one knows enough to correct them.

So, here’s my corrective.

Wait, why should we listen to you?

I’m glad you asked. There’s some frankly terrible careers advice floating around online (as well as some good stuff), so always think critically. Here are my bona fides:

• Over the last few years I hired multiple Data Analysts, Data Scientists, and Data Engineers. I also assisted colleagues with hiring. I have reviewed about 1000 C.V.s personally.
• I went through a job search last year, applying everything I’d learnt from both sides of the hiring desk. I got plenty of callbacks, so my C.V. writing skills must be at least ‘not terrible’.

Is your advice only for Data Analysts?

My advice should work for any job with a mixture of technical skills and human skills.

However, in Data jobs (Data Analyst roles especially) the importance of these two categories is particularly finely balanced. It’s essential to have key technical skills, e.g. the ability to

• apply statistical tests
• write SQL queries

and human skills, e.g. the ability to

• write a crisp summary of findings
• verbally discuss people’s problems and requirements
• work well in a team

… as well as some skills that straddle the division (making attractive visualisations tends to require technical and communication ability).

Therefore, Data Analysts need their C.V. to do quite a lot in just one page.

Writing a great C.V.

What is the objective of writing a C.V?

There are two.

First, to pass a general sift. You must imagine a nonspecialist reading hundreds of C.V.s and sorting them into piles (reject and keep) with only a minute spent on each C.V. Or worse: this first step might be farmed out to an automated system. The decision is made based on whether your skills and experience match the job description. Success here means avoiding an immediate rejection.

Secondly, to pass a deeper review by the actual hiring manager. The aim is to impress this person and persuade them that you’d do the job really well. Success here earns you an interview.

How might we meet these objectives?

Passing the sift

The first objective — passing the sift — is the main driver of C.V. design. Most job descriptions include a list of requirements. The fact that you meet these requirements (or, most of them) must be obvious. Do not make the sifter hunt for them!

Aside: I assume you do meet most of the requirements. Worth noting, some places list many skills as ‘required’ when most of them should be in a list of ‘skills that individually are just nice to have, but we don’t want to train you in all of them’. So, shoot your shot. But read the section ‘A warning’, below.

How should you design your C.V. to pass the sift?

• keep your C.V. to one page, and don’t crowd that page
• use bullet-points, not prose, wherever you can get away with it
• don’t be afraid to use emphasis
• write a ‘skills’ section and put it right at the top

That’s right — skills go directly under your name (and contact details, and that one-liner summary of yourself that’s so popular, if you like having that).

Aside: that one-liner personal summary can serve a purpose, but most people put it in because they’ve seen other people do it. It’s a good place to indicate that you fit any job requirements that don’t sit well in your skill list — chiefly markers of experience and responsibility. For example: “Highly-motivated Data Analyst with management experience, a passion for data visualisation, and three years in the industry.”

Your skill list doesn’t need to be comprehensive; include anything relevant for the job and emphasise anything that was listed in the job description.

Use the same wording as in the job description, and use both general and specific terms, e.g.

SQL (PostgreSQL, DuckDB), web analytics (GA4), …

Why do this? I’m afraid to say, at some places the first sift is entirely automated and not much better than giving some hapless intern the list of required skills and having them ctrl-F through each C.V.

(I’m told that some software of this kind only handles a single column format properly. This is a shame; personally I like a two column C.V. for its better use of space. But you’ve been warned.)

Landing an interview

If you have the skills and follow the above advice, you should make it through the initial sift. But that is just the beginning.

Now there’s a chance your C.V. ends up in front of a hiring manager, someone who understands the job you’ve applied for and already has an idea of the successful candidate in their head. Your task is to persuade them to interview you. Unlike in the sift, it’s not enough to just say ‘I have skill X’ — you have to provide evidence.

Aside: I’ve seen many candidates put ‘communication’ in their skills, only for this to be sadly belied by the state of their C.V. and/or cover letter. Treat these as an exercise in written communication!

How should you design your C.V. to land an interview?

Your C.V. must become an advertisement for you. Everything on it should have a purpose. Under your skills, add a ‘selected projects’ section:

• List 2–4 projects, big or small … just make sure they are relevant for the actual job
• Give each project a one line summary and some bullet-point details
• Namecheck the job’s required skills, showing you’ve used them in a relevant context

Focus on impact over process. You should certainly say (e.g.) ‘I automated a weekly report using SQL queries and a Python script’ … but definitely add ‘This saved a colleague one hour every week and eliminated incidents of misreported data.’

Aside: this is actually about showing your awareness of the context of your work, rather than persuading the hiring manager that the job you’re applying for is valuable. They posted it, after all.

With your projects in place, it’s time to add your work experience and education. A natural impulse is to include comprehensive detail. Resist it. By extracting crucial evidence of your skills to a ‘projects’ section you’ve freed yourself to keep your work and education sections brief; limit them to terse descriptions of what you’ve been doing with your life. A simple timeline (highlighting key achievements only) will do nicely.

The aim here is to demonstrate your record of excellence, persuading the hiring manager that you’re a safe bet—that there are no unpleasant surprises awaiting them.

Anything else goes below this, assuming you’ve got room on the page. For example, you could (briefly!) mention your hobbies or any volunteering. In truth, many hiring managers will ignore this; others really like the personal dimension. It’s a judgement call that’s unlikely to affect the outcome much.

Mock up

So what’s it going to look like? Roughly, this:

Graphic design is my passion.

This is only a slight exaggeration —the real thing should contain a little more detail than I can give for these fictional projects. While I’m being a bit flippant here and there, it should give you a good idea of the structure you’re aiming for.

(I couldn’t resist the two column format.)

Can I reuse my C.V?

No.

Consider the objectives. You can only follow my advice if every C.V. you send over is tailored for the job application. The specific requirements need to pop; the projects you choose must be relevant.

I recommend that you make a ‘default’ C.V. — you can send this to recruiters and use it as a base document. Clearly, every C.V. you send in is going to look fairly similar; for each application, create a copy of your base document and make any required tweaks. This will save a great deal of time.

Save the finalised C.V. as a PDF, cv_yourname_companyname_2024.pdf, unless instructed otherwise (always read the application instructions carefully).

(please, not default43279843.pdf: don’t let them lose your C.V. in the bowels of the company shared filesystem!)

That sounds time-consuming!

You’re right, it is.

You know what’s more time consuming? Making dozens of applications without ever passing the ‘sift’ stage.

Next time you see ‘100+ applicants for this job’ on LinkedIn, remember this: interviews are time consuming. When jobs have lots of applicants most companies just get more punitive at the C.V. sifting stage. When competition is really high, only near-perfect C.V.s (and cover letters etc.) lead to callbacks.

Say you are one of five hundred applicants. Your odds of getting that job are not one in five hundred, because the hiring manager is not pulling names out of a hat! The hiring process is supposed to eliminate randomness. It doesn’t entirely, of course, but the upshot is this: your odds are either quite good (better than one in twenty, say) or close to zero.

Which of the two it is depends on whether you can persuasively show, at every stage of the process, that you’d be a good choice for the job. You will only get to the later stages if your C.V. can get you through the door. That is why it is the most important thing to get right: it’s a prerequisite for everything else.

Can A.I. help?

Maybe!

A.I. assistants backed by large language models (LLMs) are here already; they are only going to get better. Yet, at present the internet is plagued by low-quality content mass-produced by thoughtless use of LLMs.

Just remember to use it as an assistant, not a replacement. Critically read what it writes. It may enable you to move from the role of writer to editor, but the latter is still a crucial job.

A good experiment might be to prompt an assistant with your ‘base’ C.V. and the job description, asking it to follow my advice above. Just remember the propensity of early LLMs to hallucinate (i.e. ‘confidently make stuff up’). Check that your A.I. assistant hasn’t misrepresented your skills! Which brings me to …

A warning

(Medium doesn’t give me the option to format this in sixty foot letters of fire, but if it did, I would):

DO NOT LIE ON YOUR C.V!

It is unethical. You’ll be found out at technical interview, or (worse) during your probation.

Ignore anyone who tells you ‘everyone does it’: such people get jobs only in the miserable sort of company where it’s actually true because no-one cares enough to check. They then assume that their experience is universal and the only way to get a job. They usually hate their job.

Why are you writing this now?

The late 2010s were a golden age for Data. Huge demand from companies who wanted to become data-driven met with limited supply.

A few years later, the inevitable result: salaries surged, bootcamps and MSc programs cashed in (in a gold rush, sell shovels). In 2022 I ran a Data Analyst internship; there were 500 applicants in two weeks (yes: that wasn’t an exaggeration).

It’s no secret that early 2024 is a tough time in Tech, nor that many Data teams have been let go as companies seek to remove cost centres. The shock is reverberating through the industry; Data leaders everywhere are rethinking how a company’s Data function should even work. How they can do ‘more, with less’.

During my job search last year one hiring manager candidly told me of the wonderful, experienced candidates they’d snapped up during the Big Tech layoffs — people settling for less responsible roles and suddenly in range of their limited budget. In hard times companies are even more reluctant than usual to invest resources in training juniors, preferring known quantities.

And so here we are. A glut of bright-yet-inexperienced aspiring Analysts hammering on the door, just as the portcullis drops down and the castle prepares for a siege. Let this article be your battering ram.

I don’t mean to be depressing! I truly believe that the fundamentals of the industry remain solid. Look at the stock prices for any of Alphabet, Amazon, Meta, or Microsoft. All the charts look the same: losses throughout 2022, (full) recovery throughout 2023. Other more niche, data-centric companies are similar.

The current climate is a simple (over)reaction to pandemic-era over-hiring by Big Tech, coupled with unexpected economic shocks and the return of significant interest rates.

This too shall pass. In the meantime (and especially if you are an entry-level Data Analyst) I hope this guide will help you in your next job application.