Senior Data Analyst’s Guide to Landing a Data Analyst Role

Read Time: 9 Minutes

If you’re struggling to land roles, it’s because you have some sort of skill problem. 

If you didn’t, you’d find it easier to get the role you’re qualified for. This sounds simple, and it is, but that doesn’t mean it’s easy. And I don’t say this to be mean, I have this problem too. I’m trying to identify the issue so we can work on it.

This obviously ignores outside factors like luck and bias, but we can’t control those so I’m going to leave them alone. Instead, I’ll focus on what we can control and improve on. I want you to be better equipped for job applications after reading this.

So now that we’ve gotten that out of the way, let’s figure out WHAT KIND of skill problem you have.

Is it a resume skill problem? A storytelling skill problem? A technical skill problem? 

Identifying what kind of problem you’re facing allows you to go and improve on it. 

The job searching process is a matching problem with incomplete information. 

I know this because the best possible candidate for a role, objectively speaking, regularly misses out on that role. Not because they aren’t qualified or an ideal match, but because they haven’t conveyed that information to the hiring company. They may think they have, but if their resume is a perfect match for a role, they should be selected. 

Instead what often happens is that your resume doesn’t show you’re a perfect match. Or at least not in an optimal way. Maybe you have a 2-page resume with the most important information at the very bottom, because that made sense chronologically. If only a hiring manager spent 10 minutes reading your resume, they would find this. But they don’t, so they won’t.

You need to find a way to show them that you’re a perfect fit, or as close to perfect as a human can be. 

Most people spend a concerningly small amount of time on their resume, me included. We write down our accomplishments and, because they make sense to us, we believe they make sense to someone else. But they don’t have our life story in their head. They’re reading our resume without the context of US. So they miss little details that we didn’t include, which changes the texture of a resume. It takes it out of the environment that we read it in and puts it into a foreign environment where it doesn’t land the same.

This is the first bucket of people struggling to land a role, and this is the most optimal situation to be in. Fixing a resume isn’t that hard when compared to learning a new skill like SQL. With 10 hours of researching and asking for opinions, you can have an above-average resume. With a bit of extra effort by asking 5 people to read your resume and tell you what they think your experience is, you can nail this down quite well. Yes, it takes some effort. But so does applying to 200 jobs without hearing back, and that feels much worse. I’ve done it and wish I could go back and read this article before starting my search.

The next group of people have a storytelling skill problem, and they overlap with the resume folks. A resume is just a short, bullet-point story of your work life and how it relates to the role you’re applying for. But if you include the wrong bullet points or if your bullet points aren’t a strong indicator of the match between your skill and the job description, you don’t hear back. So you need to figure out how to tell your story in a way that closes the gap between what the job description says and what your resume shows. In a perfect world, your resume would be a 100% match. Each bullet point SHOWS that you’re qualified, using metrics and concrete language that aligns with the job description. 

Fixing this is harder and this is when asking someone with more experience for help is often the right next step. But that doesn’t mean immediately paying someone else to fix the problem. Instead, read up on resume best practices and look at examples of great resumes to understand how you can incorporate those things into your own resume. My go-to here has been on YouTube, the channel Self Made Millenial is amazing and has guided me well (and I have no affiliation with it). Most people don’t seem to bother doing this step and instead shell out the cash for a fancy resume built by someone who doesn’t know their background. 

I’ve reviewed quite a few of those resumes myself. They’re great general resumes, but poor Data Analyst resumes. They focus on impressive bullet points, not the story of your life and how that has culminated in you being a great fit for this role. There’s no context, just a robotic list of achievements with the same language everyone uses on their resume. 

The next aspect of storytelling comes into play AFTER your resume has made it through the application gauntlet and a recruiter has called you for a phone screening. They’re contacting you because your resume matches closely enough with their role that they’re hoping to find that you’re an even better match than your resume shows. This is great news because you have some control over this situation. Unlike when you submit a resume and an avalanche of reasons can stop you from moving forward, now there are fewer to stop you. 

This means it’s time to figure out exactly what the hiring company wants in their role. This part is hard. Really hard. You may not know this, but job descriptions aren’t created by 1 single person with 1 single purpose in mind. They’re created by multiple people with multiple, often competing, priorities. 

Here’s how it’s gone in my personal experience.

Someone from HR/Talent Acquisition throws together the base job description with all the company boilerplate info. Things like benefits, company info, and salary in many cases now. Then it’s time for the hiring manager to get their hands on it. This is often the person who most understands the reality of the role, but they tend to get input from their team or related teams (I usually asked the heads of the other data teams). But after this initial part, a funny thing happens - emotions kick in. 

If the hiring manager had a bad hire in the past, this is where their bias shows up. Maybe the role only needs to have intermediate SQL knowledge, but the hiring manager once hired someone with 3 years who could only SELECT * and they had to go through the painful process of letting that person go. So they insist on 4+ years of SQL because that would have filtered that 1 person out last time. 

This tends to continue a bit, and other folks give similar input. So the job description starts realistic and slowly gets inflated until it’s the “perfect” role. But the problem is that now all the Intermediate Data Analysts don’t meet the criteria and the Seniors who do meet it scoff at the salary, because it’s meant for someone with less experience. 

Sorry for that aside, but after seeing this happen and having so many people ask how a job description ends up being so intense, I wanted to explain it. This is also why meeting 60% of the qualifications usually means you’re reasonably qualified. Though it matters WHICH 60% you meet.

So deciphering the role is hard to do, but not impossible. I like to look at the order of requirements and weigh them based on position. It isn’t perfect, but most folks ensure they put the important parts higher up. 

It’s also good to look at the non-required section to see if they point to anything additional. 

If they mention that you’re working closely with data engineers and that use of dbt or ETL/ELT, in general, is a big asset, then you’re probably looking at a more SQL-heavy role with fewer business-facing responsibilities. 

If instead they talk about the stakeholders you work with and put an emphasis on translating complex analyses into digestible information for a non-technical audience, then you’re likely more business-facing. 

Figuring this out is useful because it gives you a basis for your own story and how to align that with their needs. You could have an amazing resume but if the interviewers are looking for a technical person and you start talking about your process for managing adhoc dashboarding requests and how you keep business stakeholders happy, you’ve moved further away from that perfect match they’re looking for. You may have the best interview you've ever had and still not hear back. Because they needed a technical person more than the business one that you showed yourself to be.

The better you understand what they need, the better you can align yourself.

Again, simple but not easy.

Our last group of people have a technical skill problem. This isn’t the perfect way to say it, but it conveys the point more easily. 

This often comes up in technical interviews or take-homes. You’ve done great in the process so far - the team loves you and is excited to have you advance. And then you hit the dreaded technical round. 

For some of us, the issue here is a form of storytelling. We freeze up and misspeak, or we just go blank. This isn’t a technical skills problem, it’s a storytelling problem. Go find 5 people to rehearse with until you can explain all of the different SQL joins while diffusing a bomb. 

If instead you get through the technical round and feel that you explained yourself well but still don’t make it through, it’s time to examine your skills. My number 1 advice here is to ask the hiring manager if they’re willing to share feedback to help you improve. With technical skills, it’s often easy to see what went wrong. Technical interviews tend to have clear answers and any good interviewer should be able to pinpoint where your weakness is. 

“But how can I feel that I performed well and still fail a technical interview?”

A good interviewer will want you to have a good experience, even if they can tell right away that you’re not the right fit. They aren’t going to just kick you out, so they’ll help you along with some questions. Or even accept incorrect answers to keep moving forward, looking to see if there are other areas that you’re stronger in. 

I’ve interviewed people who bomb the first question and then get back on track 10 minutes later and perform extremely well. Keeping the interview moving forward and positive can bring out the best in candidates and this is why interviewing is so hard, on both sides.

Okay, back to improving. Some companies, for a variety of reasons, won’t share feedback. That sucks, but there’s nothing you can do about it. What you CAN do is find similar questions online and go find someone who would be on one of these hiring panels for feedback. Go do a mock interview with them and ask them to provide feedback on where you fall short. Shameless Plug: If you can’t find anyone, I offer this as a service :).

The final aspect of a technical skills problem is that the technical skills you need in an interview are often adjacent to those that you need in the job. The vast majority of Data Analyst technical work is rather routine and the complexity isn’t in the techniques used but in the data itself. The hardest projects I’ve ever worked on were hard because the data was messy or the business rules around the data were murky. Figuring out why Customer #199 had 200 sessions this month when a session has to last a full day is much harder than joining the customer table to the app sessions table. 

So you need to practice technical interviewing even if you’re extremely technically competent on the job.

I’ve never had to write SQL in a text file where I can’t run it while on the job. But I’ve seen that in a lot of interviews. Or having to think out loud and explain my thinking when normally I just break the problem into small chunks and handle them 1 at a time, often writing code and iterating once I see that my results are wrong (which I expect on the first try). We suck at things we rarely do, but we can improve rapidly with some concentration and good direction. 

Even taking 2 hours to practice thinking out loud and writing SQL outside of a proper environment will make you significantly better if you haven’t done that before. It’s honestly pretty jarring at first.

There’s no need to feel bad about yourself if you’re struggling with interviews. We all have and will continue to struggle. The process is murky and far from standardized across companies and even within companies. Couple that with a matching problem and incomplete information and it’s no wonder that interviewing is so hard. I know all of this and I STILL struggle much more than I’d like to admit. 

All you can do is improve and keep moving forward. In doing so, you’ll continually improve your odds of landing a role until you eventually do. And if you’re reading this then you’re doing more than the average person will. That’s something to be proud of.

— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — —

Whenever you’re ready, here are 2 ways I can help you:

1. Data Analyst Launchpad - My course on how to build a resume and cover letter that gets results. I share 7 years of data analyst experience, including interviewing and hiring for most of 2023.

2. A Coaching Call - If you’re struggling with applications or want to level up your skills, I’ll give you a plan to get there and the resources that I’ve used to get there myself. If you’re unsure if I can help, shoot me a message and I’ll provide any guidance I can right then and there, no cost to you!