Breaking Into Data: How to Land A Job in a Competitive Market

If you’ve been keeping up with the job market, then you’ve probably seen stories about single roles receiving hundreds if not thousands of applications within a few days, leaving hiring managers overwhelmed.

So, how do you stand out in such a saturated market?

How do you land a job—let alone your first role in the data world?

I remember facing those same questions and feeling pretty hopeless. I was only six months away from graduating, with no technical work experience beyond teaching kids to code, and had already sent out hundreds of resumes. I managed to land a single interview, but being my first technical interview, I didn’t do so hot.

I get how discouraging it can feel just hoping someone will notice your resume and give you a chance.

If you’re starting your job search or are about to finish school, I want to share some tactical tips and personal insights to help you break into the data industry.

1) Don’t Get Distracted By Job Titles, Remote Jobs or By Companies

When I was job hunting the second time around, I didn’t even know “data engineer” was a job title. I searched job boards by skill sets instead—things I knew I had done before, like automating workflows, using SQL, and building tables in a data warehouse.

Job titles evolve.

What used to be called an ETL Developer might now be labeled Data Engineer with more expectations. Roles morph, overlap, and get rebranded all the time. If you get too caught up in chasing a specific title, you could miss great opportunities that match your skills.

The same goes for companies. Like many in Seattle, I was laser-focused on landing a job in big tech, especially since, at the time, Amazon was basically spamming job boards. Every other job was at Amazon.

But here’s the thing—there are plenty of options:

• Startups

• Smaller companies

• Healthcare organizations

• Local companies in general

To name a few.

These companies often have fewer applicants and equally challenging problems to solve. So, while applying for big tech jobs can be part of your plan (if that’s your goal), don’t limit yourself.

Your first job doesn’t have to be flashy with a salary in the 99th percentile right out of the gate—it just needs to be a place where you can grow and learn.

2) If You Fail To Prepare, You’d Better Prepare To Fail

Source

“What should I study for my interview in two days?"

I get messages like this all the time—whether during live sessions or in my YouTube comments. Honestly, it stresses me out for those frantically cramming. I can only hope they’re asking for last-minute topics, not just starting to prepare.

Technical interviews are tough. They typically cover a mix of programming, SQL, system design, statistics, and more. The last thing you want is to panic-study a dozen topics. But it’s also easy to feel overwhelmed by everything you can review. Here’s how to focus your efforts effectively:

• Create a study plan

• If you’ve got an interview, ask your recruiter for topics

• Find a group to keep you accountable

Don’t wait until the last minute to study. While some interviews might dive into role-specific technologies, most data-related interviews will test your SQL skills and often include light programming challenges. Preparing in advance ensures you’re ready for these core topics and can focus on areas that matter most.

Remember, you’ve worked hard to get this interview opportunity—it’d be a shame for you to lose it because you didn’t start preparing until the interview date was set.

If you’re looking for interview guides, here are a few you can use:

• Data Engineer Interview Guide

• Data Science Interview Guide

3) Relationships And Referrals

As the world leans further into AI, genuine, in-person relationships are becoming more critical. Our inboxes are overflowing with spam emails from B2B SaaS salespeople, and the constant noise online can make meaningful connections feel rare. Because of this, reverting back to more personal, face-to-face relationships seems inevitable.

I've already had multiple hiring managers reach out to me for referrals after trying the traditional route of posting job listings. They received so many resumes that sorting through them all became challenging. Instead, they turned to their networks, seeking recommendations from people they trusted outside their companies.

So, how can you start building up your own network and professional relationships? Here’s where you should begin:

• Attend Data Events in Your Area: Check platforms like Meet-up.com or ask industry peers about events they recommend. I’ve made a lot of connections through going to different meet-ups and events. I would recommend you don’t go in with the mind set that the people you are talking to owe you a job. It often can feel transactional to the people on the other side.

• Reconnect With Classmates: The mistake most people make when it comes to networking is they only reach out when you need something. So be better! Stay in touch with classmates or past colleagues. Check in on them. Ask them how they are doing, grab coffee, and be genuine.

• Leverage LinkedIn: LinkedIn is more than a place to post about how you just landed a new job. It’s also a great place to connect with people. However, simply sending your resume to someone and expecting a job offer won’t get you far. In fact, you can probably find several posts on LinkedIn complaining about that kind of behavior. Instead, engage with posts, comment thoughtfully, and share your learning experiences. By being active and authentic, you’ll make more meaningful connections that can open doors over time.

• Participate in Online Communities: There are countless data communities where you can engage with peers and discuss tools like dbt, SQLMesh or Airflow, as well as broader topics in data engineering and data science. Join these groups, ask thoughtful questions, share your progress, and contribute where you can. Avoid lurking—getting involved is how you’ll build a reputation and professional relationships.

Once you start meeting people, here are a few steps to help solidify and grow those relationships.

• Ask Thoughtful Questions: When connecting with new people, show genuine curiosity about their experiences and perspectives. If the only thing you care about is getting a job, it shows. People will likely be turned off.

• Follow-Up After Initial Meetings: If you've attended an event or had a conversation, follow up with a brief email or LinkedIn message. Honestly, most people don’t do this. Maybe you get busy or are afraid of what they’d think.

• Be Intentional About Networking: I find that many people only passively network. They go to an event and meet maybe 1-2 people. Don’t get me wrong, your goal shouldn’t be to try to talk to everyone. But networking requires effort. You need to try to talk to a more than just one person. So be active at events, talk to people, find out who is managing the event, get to know them, etc.

• Say Yes to Opportunities: If someone invites you to an event or introduces you to a new connection, take the opportunity. Expanding your circle often starts with being open to new experiences.

4) Obscurity Is the Enemy

I swear, it feels like everyone needs to be a brand or influencer just to get noticed. Honestly, I don’t believe that’s true—but I do agree with other writers(see

Joe Reis

) who say obscurity is the real enemy when it comes to advancing your career.

That doesn’t mean you have to pump out content constantly (though sharing what you’re working on can be a powerful way to build visibility). Instead, here are some effective ways to avoid staying obscure:

• Engage online—join different Slack groups, get involved, and be helpful.

• Contribute to open source.

• Write content and share what you’re learning.

• Go to events and contribute to conversations.

If you’re not consistently doing one or more of these things, you risk losing out to those who are. Personally, I focused on writing and attending events—I wanted to get involved, learn, and grow.

Need a little extra motivation? Check out Never Eat Alone. Yes, it’s a bit of a cheesy business self-help book, but it does help motivate you to step out of your comfort zone.

At the end of the day, if no one knows you exist and if you’re relying solely on job applications, you could be waiting a long time to land your next role. Visibility matters—start taking steps to get noticed.

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Before we dive into the more tactical side of interview prep, I’d also like to add a quick tip from my friend Abhishek Gupta who has landed jobs at Facebook, Lyft and Stripe.

I would say be able to showcase end-to-end projects. They don’t have to be super polished, but if you can talk about how you got data from a source system, to a downstream table, transformed it using airflow, and built something preliminary using superset, then I think the person interviewing would get a lot more confidence.

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5) Tactical Steps For Interview Prep

Source

Let’s talk tactical steps—what can you do today, next week, and over the next few months to land a job? Start with the basics:

• Make Sure the Basics Are in Order:

  • Resume: Tailor it for each job, keep it ATS-friendly, and focus on achievements rather than tasks. Have someone you trust review it for clarity and impact. Also, I will say using tools like ChatGPT can help you improve your verb choice and also point out where your bullets are weak.

  • Portfolio: If you don’t have projects, now is a great time to start putting them together. You don’t have to have a fancy portfolio website, but at least put together a Github project and show them off.

  • LinkedIn Profile: If you are using LinkedIn to comment, post and engage, make sure you clearly show what your skill sets are. Share Github links, talk about past projects, have a buttoned up profile, etc.

• Track Your Progress: One of the biggest challenges during a job search is staying motivated. Often, the struggle comes from not keeping track of your efforts. I have had conversations with people who think put in a lot of effort but have only put out about 20-30 job applications and haven’t even gone out of there way to meet new people. I get it, it is a hard market, but you can’t expect jobs to just fall into your lap. So ask yourself, how many applications have you actually submitted? How many meet-ups have you attended? Keep track of:

  • Applications Sent

  • Meet-ups

  • People You Meet and Follow-Up With

• Study and Prepare:

  • Create an Interview Checklist: Don’t study randomly—build a checklist of topics, questions, and resources, marking them off as you go(like the ones provided above).

  • Mock Interviews: Practice both technical and behavioral questions to build confidence. The best way to do this would be to get a friend who is also technical to interview you. Colleges also often offer options here where you can go to the career center and have someone interview you.

• Keep Yourself Accountable:

  • Set Weekly Goals: Define clear, achievable targets like “apply to 10 jobs,” “reach out to five people,” or “attend one networking event.” Many job seekers say, “I am trying to find a job,” but without a structured plan, they often fall into the trap of sporadically sending out resumes without consistent effort.

  • Celebrate Small Wins: Recognize milestones along the way—getting an interview, connecting with a key contact, moving past the first phone interview, or advancing to the technical round. Many people overlook these moments, but each step forward is progress worth acknowledging.

Final Thoughts

Landing a job—especially your first—has been tough for a while. I remember when I was trying to break into data, people struggled with similar issues.

• Entry-level jobs required 2-3+ years of experience.

• ATS filters rejected resumes before a human ever saw them.

• The surge of interest in becoming a data scientist saturated the market.

Now, we’re also competing for remote jobs and facing the added challenge of LLMs churning out thousands of generic resumes. However, challenges will continue to exist and evolve. That doesn’t mean you should throw your hands up in defeat. If you have the right skills and you can pass an interview, trust the process.

Focus on building genuine relationships, skills, and pushing yourself without losing heart.

Wherever you are in your job-searching journey, pick a few tactical steps from the list above and execute. Right now—find a meet-up and attend the next event, join a data community, spend time getting to know the space, and when you’re ready, start sharing what you’re learning.

With that, I want to say thank you for reading.

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Video Of The Week - Understanding Data Engineering in 2025

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Articles Worth Reading

There are thousands of new articles posted daily all over the web! I have spent a lot of time sifting through some of these articles as well as TechCrunch and companies tech blog and wanted to share some of my favorites!

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Incremental Load vs Full Load ETL: Key Differences + Examples

ETL and ELT are two widely used data integration techniques. Both methods involve extracting and consolidating data from multiple sources to a single destination. Many organizations prefer the ETL method: it is more compatible with legacy systems and supports transformations before loading, providing more control over data quality and consistency.

Choosing the right data loading strategy is critical for efficient ETL processing. There are several types of data loading processes, including full refresh, full load, and incremental load.

Here, you will find a detailed comparison of incremental load vs full load. Using this information, you can select the loading approach that is better suited to optimize your ETL pipeline and meet your organizational requirements.

Read More Here

How to tell a story with data

By

Torsten Walbaum

and

Tessa Xie

As long as humans have existed, they have told stories. For example, the cave paintings in Lascaux, France, are over 30,000 years old and show traces of early graphical storytelling.

And stories still play a central role in our lives today. Ask yourself:

• How much do you remember from the last slide deck you saw at work? Probably not much

• But how much do you remember from The Lord of the Rings or The Office [or whatever book / movie you like]? Chances are, you can quote entire scenes

If you want to get your point across and make a lasting impression, you need to wrap it in a narrative. Sometimes, people who work with data think they’re an exception to this. Data represents cold, hard facts – that should be convincing enough, no?

Read More Here

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End Of Day 161

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