Why I turned down 3 dream jobs at Amazon (and ultimately left)

Lessons from a wild year of career soul-searching.

Imagine reaching the summit of a mountain you've been climbing for years, only to ask yourself, "Is this it?" That's exactly where I found myself in 2021 after being promoted to Senior Data Scientist at Amazon.

The LinkedIn update was immediate, and the congratulations poured in, but as the initial excitement faded, a familiar voice haunted me: "Now what?" The idea of another promotion cycle suddenly felt exhausting, as I’ve done this twice.

No one told me I had to know the answer right away, but I was so used to eyeing something new immediately after reaching a goal — the perfect recipe for burnout. Today, I’ll talk about 3 roles in Amazon I explored during my final year. I’ll share my entire thought process and explain why I eventually quit Amazon.

This takes about 15min to read. Have your cup of coffee ready ☕️.

While the path to the senior data scientist is more straightforward, the path beyond the senior level is less defined. If you want to stay in data science, you typically face two options:

1. the IC (individual contributor) route to the Principal Data Scientist

2. the leadership track to management.

While pondering what was better for me on a Friday afternoon, I chatted with a coworker about weekend plans. He said he was excited about reading a new research paper on Saturday. His eyes literally lit up when he talked about the paper. Me? I'm itching to finish the Disney CEO (Bob Iger) biography, and I'm more fascinated by business negotiation stories than cutting-edge ML algorithms.

This small conversation made me realize how different I am from a passionate data science researcher. Although I could probably work hard and grow to the principal level, it’s not where my superpower lies.

This moment really hit me.

It’s the first domino in a series of realizations that will reshape my entire career trajectory. I want to find my niche, a path that feels like play. To find this ideal path, I spend a lot of time thinking, "What's something hard for others but easy for me to do?"

My New Career Criteria

I still enjoyed data science, but after seven years, I craved something that involved more strategic thinking, relationship-building, and less hands-on work. It was also the year I grew to 100k followers on LinkedIn and started my podcast. I often felt like I was juggling two jobs and couldn’t keep up with my never-finished to-do lists.

The team where I got my promotion was perfect for rapid career growth. Being customer-facing, we had to constantly deliver machine learning solutions within 3-6 month cycles. This environment provided many opportunities to practice various algorithms across different industries. However, the short project cycles also made it the most stressful team I'd ever been part of. After two years, I could not maintain that intensity. But I still want to stay at Amazon because I have already built my network here, and exploring other roles is easy.

It doesn’t matter what path I choose; the first decision was clear — I need a different team within Amazon. So, I came up with three criteria to help me find this ideal role:

1. No tight deadlines and a more flexible schedule

   1. Allow me to have energy working on content creation after work

2. More leadership, strategic thinking, and less hands-on data science work

3. It values both my technical skills and my communication skills.

   1. Ideally, this can create a win-win situation for my day job at Amazon and my side hustle.

With these, I started a journey through three different roles, each teaching me a unique lesson. Buckle up, because this ride through the corporate jungle is about to get interesting 🎢

My first stop:

Data Science Manager

Looking back at that Friday chat, I pretty much crossed off the principal data scientist path right away. Management seemed like the obvious way to go.

· The Hope

I thought being a manager would be awesome - handing off work, cooking up big-picture strategies, and mentoring people. You know, all the stuff I love. So, I hit up a manager friend at Amazon and spilled my wishlist. He was like, "Hey, I know just the team for you!" Next thing I know, I'm interviewing for a gig in the Shipping org.

The whole process was quick, and I clicked with the manager. He is a product management guy who gets data science and ML. Plus, he wanted me to own some high-impact projects. Besides, he was totally cool with me keeping my podcast going. He said, "Work on your own time, no worries."

So, after two awesome years, I had to say goodbye to my old team. It was bittersweet, but I was ready for a new adventure. Next thing I knew, I was diving into this supply chain management team, ready to build a kick-ass data science team from scratch.

Some context about Amazon’s manager development process: you don’t grow to be a manager right away. First, you need to act as a tech lead manager, managing a team while still doing some hands-on work. So, my manager wanted me to do 30% to 50% hands-on work and gave me one direct report to start.

It sounded perfect: less hands-on work, more flexible time, the opportunity to shape a team's strategy, and a new domain to learn and grow into. What could go wrong?

· The Challenges

1. The Mental Juggling Act

From day one, I was thrown into the deep end — stakeholder meetings, planning roadmaps, and reviewing docs left and right. Talk about a reality check! I realized pretty quickly that I'd totally underestimated what management was all about. The stress didn't go away; it just changed its outfit. I now grappled with ambiguity, team goals, and my direct report's success.

The flexible schedule I dreamed of was a mixed bag. Sure, I could step away to record a podcast, but my brain was always on duty. Back when I was just responsible for myself, I could silence Slack for hours for focused work; as a people manager, I needed to be on standby, especially to help out my new direct report when he got stuck.

2. The Administrative Burden

I dreaded writing review documents for my direct report - a common struggle for many managers. It's like trying to write a Yelp review for your best friend's cooking - you want to be honest, but you also don't want to hurt their feelings. And that's just the tip of the iceberg. Throw in salary talks, legal mumbo-jumbo, and immigration paperwork, and suddenly, I'm drowning in a sea of admin tasks.

3. Domain Knowledge and Passion Mismatch

Here's the kicker: even in data science, different domains are like different planets. Supply chain management is all about optimization algorithms, which I'd never touched before. I was basically learning a new language and rocket science at the same time.

The real gut punch is that I just couldn't get fired up about our business problems. In my previous roles, I was that nerd working on projects over weekends. Now, watching all-hands meetings from my couch, I felt numb to our business goals.

· The Silver Lining

Don't get me wrong, it wasn't all doom and gloom. Mentoring my direct report was definitely the highlight. I got a real kick out of helping him map out projects, nail milestones, and push his boundaries.

I found real satisfaction in watching him grow.

· The Decision

However, while I enjoyed mentoring people, it was not enough to sustain me in the full scope of a managerial role. It was a bittersweet reminder that sometimes we can excel in certain aspects of a role without it being the right fit.

I shared my delemma with my manager, explaining although I enjoyed working with him, my direct report, and my peers, that I couldn't balance my creative drive with the demands of the role. To my surprise, he suggested I create content within the team to scratch my creative itch. While I appreciated his effort, I knew the team needed someone fully dedicated to supply chain optimization - someone who could get excited about the nitty-gritty details. That someone, I realized, wasn't me.

The decision to leave after just three months was agonizing. My manager even offered a raise, but I knew it wouldn't fix the fundamental mismatch.

· The Lessons

As I started to look for other roles within Amazon, I reflected on what I'd learned.

1. Managing isn't just about developing people; it's about developing strategies you believe in—and being willing to live with the constant mental juggling act.

2. Passion for the business is crucial for effective management, at least for me. Without genuine excitement for the problem space, it's challenging to sustain the energy required for leadership.

3. Delegation of work and flexibility of schedule don’t translate into reduced stress. Writing documents, attending meetings, and developing strategies require significant mental effort. Ultimately, you own the team's goals and outcomes.

I could not have learned these lessons by reading about others' experiences; I had to step into the role to truly experience them for myself. So, I don't regret trying it.

Months after I left the team, I received an appreciation note from my former direct report sharing the news of his promotion. That moment of pride and happiness confirmed that while I decided not to grow into a data science manager, I had made a positive impact during my brief tenure. It also reinforced my passion for coaching and mentoring, a skill I would continue to develop and eventually turn into a career.

After realizing that neither data science management nor the principal IC path was for me, I looked beyond traditional data science roles.

Enter my second try…

The Developer Advocate

Inspired by data science content creators who became developer advocates or tech evangelists, I explored this hybrid role that blends technical expertise, public speaking, and product marketing.

· The Hope

I reached out to the Developer Relations team at AWS. The team publishes O'Reilly books, creates tech tutorials and even Coursera courses, and speaks to thousands of people. "Finally, a role where my content creation skills and technical knowledge can merge!" I thought.

· The Challenges

This time, I decided to do my homework. I mean, this role was pretty different from my usual data science stuff. So, I started chatting with folks who'd been there, done that. Wow, I was surprised. My first convo was with this senior developer advocate at Amazon who was about to jump ship back to engineering. Talk about timing! He spilled the tea, and after chatting with a few more people, I started to see a pattern. Turns out, this dream job had four big hurdles that nobody was really talking about.

1. Career growth bottleneck

The hybrid nature of the role, juggling between tech and marketing, make it difficult to grow in a company. Reporting to the marketing team often leads to a decline in technical sharpness. For those who joined out of love for tech and sharing knowledge, the heavy marketing focus can feel unfulfilling. Unless you're aiming for a marketing leadership role, advancing to a tech VP or principal engineer position becomes increasingly difficult.

2. Difficulty in measuring impact

Success metrics in this role are often not well-defined. Some teams focus on event attendance or social media views and end up pitting team members against each other as they compete for audience attention.

Compounding this issue, leadership often struggles to define success: Is it measured by session attendance, social media engagement, or revenue impact? The long-term nature of top-line marketing's value often clashes with companies' desire for immediate ROIs, creating a lot of stress and confusion for the team.

3. Burnout

The role demands a diverse skill set: creating tutorials, giving talks, and providing product feedback. Sometimes, you have to travel to conferences. With frequent leadership turnover and shifting goals, the workload seems endless. Almost everyone I spoke to mentioned feeling burnt out.

4. Conflict of interest

As a content creator with my own platforms, I was concerned about potential limitations on my personal expression. Some developer advocates shared that their companies restricted them from discussing competitors' products on their personal social media, highlighting the blurred lines between professional and personal brand in this role.

· The Lessons

Content creation for yourself is art; for a company, it's commerce. Representing a company's products and driving revenue requires a completely different skillset than personal expression.

Realizing it might not be as glamorous as it seems, I decided not to transition into this role. But I still learned 3 valuable lessons:

1. Your passion project isn't always your ideal profession. Just because you excel at something doesn't guarantee it'll be a fulfilling full-time job. A large social media following doesn't equate to enjoying social media as a career.

2. Your daily tasks matter, but your success metrics rule. Understanding how your performance is evaluated is as crucial as knowing your day-to-day responsibilities.

3. Don't fall in love with a job description; fall in love with the reality! Meet the team, talk to your network, and don't let the allure of a trendy title cloud your judgment.

Here comes my final stop at Amazon:

Machine Learning Instructor

· The Hope

As my connection to business goals waned, I turned my attention to a role in the AWS training team. The ML instructors give full-day training sessions to AWS partners, with no hands-on work and a structured 9-to-5 schedule, which seemed like the perfect solution. I'd always enjoyed teaching data science and machine learning, often playing the teacher role in my previous positions. Although this role offered less leadership and strategic thinking, it promised the work-life balance I craved.

I thought, "This is it - a chance to share my knowledge, use my communication skills, and finally turn off my brain after work."

Third time's the charm, right? The interview process involved a technical presentation using storytelling techniques. I prepared for a week using one of my previous ML project and nailed that interview. I knew I could do it.

· The Challenges

1. Creative Constraints

This reality hit hard once I started. The course material was fixed, leaving little room for creativity. While I could add personal anecdotes, the bulk of the content was predetermined. The course creation team is a separate team, they take our feedbacks, but there is a length process. I couldn’t use my creativity to change the structure or topic.

2. Repetitive Nature

Basically, I totally underestimated how much the repetition would get to me. Picture this: you're teaching the same 3-day course over and over. It could get old real fast.

Even in my current "data science career accelerator," I've learned to pre-record the fundamentals and focus on the fun part - interactive coaching. But in this gig? Even though we had some Q&A time with students, most of the job was just repeating the same slides, day in and day out. I’m sure I could keep improving my delivery skills, but it’s too static for me.

· The Decision

I grappled with the guilt of a quitter, again. But I had to trust my instincts. I knew I wouldn't be truly happy in this role, even if I could perform it adequately.

I feel a bit ashamed that when I was joining this team, deep down I hoped for a role that I could “coast”, I could do the bare minimal, but I realized I wouldn’t respect myself if I did that — either do it well or don’t do it.

· The Lessons

• Creativity isn't a luxury, it's a necessity for me.

I severely underestimated my need for creative freedom at work. A role without room for innovation, no matter how comfortable, quickly becomes stifling.

• I’m not built for 'coasting'

I thought I could find a role where I could "coast" and then focus on my passions after hours. But I'm not a robot - I can't just switch off my brain at 5 PM. If I'm not passionate about my 9-to-5, I waste my time and energy.

I get it, not everyone can work on what they truly enjoy. Sometimes, in the early stages of a career, you need to do things you don't always enjoy to learn. But after 7 years at Amazon and building a social media following, I realized I had options. I want to dream bigger. It was time to leverage my experience and platform to craft a path that aligns with my passions.

What finally made me decide to leave Amazon…

This experience made me realize that my journey at Amazon had come to an end. After seven years, leaving felt like losing a piece of my identity—as ridiculous as that sounds—but I knew it was necessary.

Throughout this wild ride, a recurring theme emerged: my struggle to balance my growing side projects—my podcast and LinkedIn following—with my full-time role. Each career move was an attempt to find a job that would complement, not compete with, my personal brand and creative pursuits.

I started to ask my network to help me find such a role, and I met the founder of an ML startup. He crafted a position tailored to my skills: a blend of senior data scientist, open-source developer advocate, and tech content creator.

This opportunity proved that sometimes the ideal role isn't found; it's created.

Although my time in this new role lasted only a year, it was exactly what I needed at that point in my career. I’ll write about this experience in the future.

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Final Thoughts

This journey taught me valuable lessons about balancing passion, integrity, and professional growth:

1. The career compass should be your superpower, not other people's expectations.

After I got promoted to senior data scientist, I was wondering if the typical data science career path still fits me. So, I asked myself, "What's something hard for others but easy for me to do?" This question prompted a wild year of career soul-searching within Amazon.

It wasn't easy to know the answer, but it’s the only way to 10X your career growth when the game is in your favor.

2. Quitting isn't failing, it's recalibrating.

I explored 3 different teams before I finally quit Amazon. I felt like a quitter. But I learned that quick job changes aren't signs of failure, but of self-awareness and courage. "It's better to be a quitter than a bitter." Always give your 100%, but be honest when it's not the right fit.

3. Don't fall in love with a job description; fall in love with the reality.

Meet the team, talk to your network, and don't let the allure of a trendy title cloud your judgment.

Understand not just your daily tasks, but how your success is measured in the team. If you find the success metric too stressful, you won't enjoy the day-to-day even if it's your "dream job".

4. “Coasting” is unrealistic.

I thought life would be easier if I could just find a team to coast. Doing the bare minimum might seem tempting, but it erodes self-trust and long-term confidence. If you feel like coasting, maybe it’s a sign of burnout, or you need a new job.

5. Don't climb the ladder. Build your own.

Don’t be afraid to tell people what you truly desire in a role. Maybe someone is looking for the exact combo of skillsets you have. It's not rare when a manager crafts a unique role to attract talent.

To be clear, I still believe it's important to know how promotion works and to get the recognition you deserve. But at the end of the day, you should be the architect of your own career. That’s why I cover both aspects in my coaching course.

These lessons aren't just my story—they're a roadmap for anyone feeling stuck, undervalued, or uncertain in their tech career.

Sometimes, the perfect role isn't found—it's crafted.

Phew! What a ride, right? If my story resonated with you, or if you're feeling stuck in your own career, I've got something that might be useful for you:

I'm running my "Data Science Career Accelerator" again, which will give you more specific guidance on how to get stakeholders' buy-in, create more impact, and get the promotion you deserve so you can become the senior data scientist every manager wants.

Space is limited. Enroll now and get a 15% early bird discount: https://maven.com/dalianaliu/ds-career?promoCode=dscareer15

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Does this journey resonate with you? I'd love to hear your thoughts in the comments.