data job

Data Job Trends: 16 Tips for Knowing When to Go

By: admin | August 15, 2025 | 6 mins read

Ever worry that you might be hanging onto your data job for longer than you should? You’re not alone. This is one of the most misunderstood questions out there. A lot of people think job security is a given, but here’s the cold truth: Your job is never 100% safe. Even the “secure” gigs can change in a flash. If you think your job is good for now, you still owe it to yourself to ask, what comes next?

Let’s talk about when to leave your job, how to know it’s time, and how to get prepared before things hit the fan.

Tip 1: Make Your Move Before You “Need” To

A mentor once dropped this truth bomb: “Raise capital when you don’t need it.” That advice runs deep. Get a lawyer before you’re in legal trouble. Hire a trainer before you’re out of shape. It’s easier to make a plan while things are calm. Prepping early means less drama down the line.

Tip 2: Stay Ready, So You Don’t Have to Get Ready

Managers rarely pull you aside and say, “Hey, you should start shopping for new roles.” Companies have their own interests. If you start thinking about leaving only when you need to, you’re late. Start building your network and skills now, not when your back’s against the wall.

Tip 3: Avoid These Mistakes That Keep You Stuck

  • Waiting for signs that are too obvious to ignore
  • Forgetting to define what makes a job “good enough” for you
  • Skipping out on learning and interview prep until you’re desperate
  • Trusting that good work alone is enough

Tip 4: Try the 1-Hour Anxiety Rule

Worried about the job market, layoffs, or your future? Give yourself one hour a day—the 1-hour anxiety rule—to feel anxious and run through your worries. The rest of the day, you’re back in action mode. This trick keeps you productive and stops stress from taking over your life.

Tip 5: Watch for Yellow and Red Flags

Think of yellow flags as early warning signs. These add up over time until you hit a red flag—a non-negotiable. List your yellow flags, track them, and set your own limit:

  1. Spot a yellow flag
  2. Add it to your list
  3. When too many build up, treat it as a red flag and plan your exit

Everyone’s threshold is different. What’s important is knowing your number.

Tip 6: Write Down Your Non-Negotiables

Writing things out brings clarity. Like relationship dealbreakers, you need a list of work non-negotiables. This way, when something’s off, you don’t spend months or years debating whether to leave.

Tip 7: Chris Garzone’s Three Non-Negotiables

Getting paid what you deserve: If your paycheck doesn’t line up with the value you bring, it’s time to think about moving on. Know your worth, calculate what you bring to the table, and stick to it.

Continuous learning: If you aren’t learning something new, your motivation tanks. Boredom seeps in. Make sure your job feeds your curiosity.

Respect and connection with coworkers and managers: If you don’t actually like or respect the people you work with, even a fat paycheck won’t save you from burnout. Positivity at work goes a long way.

Tip 8: Know Your Tipping Point

Grab a pen. List your three non-negotiables and five “nice to haves.” If less than 70% of these are being met, start looking. If you hit the 50% mark, ramp up the search. If you wait until only 30% of the job still works for you, things get rough.

Tip 9: Examples of Yellow Flags at Work

Pay attention to these, because too many mean bigger trouble ahead:

  • Your company is cutting costs, missing targets, or talking about layoffs
  • Your team’s output is dropping or morale is bad
  • People around you aren’t as sharp or motivated as before
  • Projects are stagnant, boring, or stuck in old tech

Tip 10: Your Team Shapes Your Future

You are the average of the five people you spend most time with. If your group isn’t growing, chances are you’re stalling too. This is doubly true for anyone in tech or data.

Tip 11: The Numbers Don’t Lie

Here’s a stat that might surprise you. At Data Engineer Academy, after talking to over 10,000 people, around 40% are either laid off or at risk. That’s almost half. Keeping your eyes open today means fewer surprises tomorrow.

Tip 12: Upskill Before You Need to Jump

Learning when you’re out of work is stressful. Start upskilling now, not later. There are so many ways to get ahead. Check out free data engineering courses at Data Engineer Academy and see where your gaps are.

Tip 13: You’re Always Betting on Yourself

Switching jobs? Starting a business? You’re betting on yourself every time—even if you don’t run your own company. Back yourself up with the skills and confidence you build outside of job emergencies.

Tip 14: Plan Your Steps, Don’t Wait for Emergency

Have a plan. List warning signs. Map your exit. Get your resume updated, schedule calls, start learning. Don’t wait for things to break before you act.

Tip 15: Use the Framework to Decide

Check your flags and your list of non-negotiables once a month. Notice when things don’t add up, and take action before you’re forced to. A system keeps you honest and helps you avoid drifting for years in an unhappy job.

Tip 16: What’s On Your List?

Take a minute to reflect. What yellow flags or deal-breakers are you ignoring at your job? Add yours in the comments and explain the first step you’ll take today. This is your move—be real about it.

Keep Your Career in Your Own Hands

You don’t have to wait for problems to show up. Upskill, reflect, and keep an eye on what matters to you. The best thing you can do is to get prepared today, not when you’re out of options.

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Frequently asked questions

Haven’t found what you’re looking for? Contact us at [email protected] — we’re here to help.

What is the Data Engineering Academy?

Data Engineering Academy is created by FAANG data engineers with decades of experience in hiring, managing, and training data engineers at FAANG companies. We know that it can be overwhelming to follow advice from reddit, google, or online certificates, so we’ve condensed everything that you need to learn data engineering while ALSO studying for the DE interview.

What is the curriculum like?

We understand technology is always changing, so learning the fundamentals is the way to go. You will have many interview questions in SQL, Python Algo and Python Dataframes (Pandas). From there, you will also have real life Data modeling and System Design questions. Finally, you will have real world AWS projects where you will get exposure to 30+ tools that are relevant to today’s industry. See here for further details on curriculum  

How is DE Academy different from other courses?

DE Academy is not a traditional course, but rather emphasizes practical, hands-on learning experiences. The curriculum of DE Academy is developed in collaboration with industry experts and professionals. We know how to start your data engineering journey while ALSO studying for the job interview. We know it’s best to learn from real world projects that take weeks to complete instead of spending years with masters, certificates, etc.

Do you offer any 1-1 help?

Yes, we provide personal guidance, resume review, negotiation help and much more to go along with your data engineering training to get you to your next goal. If interested, reach out to [email protected]

Does Data Engineering Academy offer certification upon completion?

Yes! But only for our private clients and not for the digital package as our certificate holds value when companies see it on your resume.

What is the best way to learn data engineering?

The best way is to learn from the best data engineering courses while also studying for the data engineer interview.

Is it hard to become a data engineer?

Any transition in life has its challenges, but taking a data engineer online course is easier with the proper guidance from our FAANG coaches.

What are the job prospects for data engineers?

The data engineer job role is growing rapidly, as can be seen by google trends, with an entry level data engineer earning well over the 6-figure mark.

What are some common data engineer interview questions?

SQL and data modeling are the most common, but learning how to ace the SQL portion of the data engineer interview is just as important as learning SQL itself.