I love to teach. I post about #dataengineering and #mentalhealth daily. 

Many years of data engineering experience doing growth analytics at Facebook, security infrastructure at Netflix, and pricing and availability infrastructure at Airbnb. 

You can find me by looking up EcZachly on all social networks 

Join my Data Learning Platform https://www.dataexpert.io

Zach Wilson

Databricks is going to win “data” the same way that Meta won “social media” 

They are uniquely positioned because of the convergence of a few trends:

- the death of the data analyst
We are moving away from analytical architectures and towards action-oriented architectures. Instead of “the data engineer does ETL so analysts can look at it” it will be “the data engineer creates context for AI agents to do something” Actions will reduce timelines and speed up business velocity. Humans will no longer be the analytical bottleneck. 

- the crushing of data siloes 
Open table formats like delta and iceberg will make business able to do things that were previously though impossible! We’ll finally have all the context for our AI in one place. Entire business context + Agents = a waterfall of business value! 


I’m launching a paid Databricks boot camp on February 9th. Send me a DM with the word Databricks and I’ll get you the details!

Founder @ DataExpert.io | ADHD | 500k+ followers | Angel Investor

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The first speaker lineup for the analytics engineering boot camp is going to be so amazing! 

These sessions are what truly separates my boot camp from “just another data course”

Data modeling is far from dead! It’s actually more relevant than ever. 

There’s been an interesting shift in the seas with AI. Some people saying we don’t need to do facts and dimensions anymore. This is a wild take because product analytics don’t suddenly disappear because LLM has arrived.

It seems like to me that multi-modal LLM is bringing together the three types of data:

- structured

- semi-structured

- unstructured

Dimensional modeling is still very relevant but will need to be augmented to include semi-structured outputs from the parsing of text and image data.

The necessity for complex types like VARIANT and STRUCT seems to be rising. Which is increasing the need for data modeling not decreasing it.

It feels like some company leaders now believe you can just point an LLM at a Kafka queue and have a perfect data warehouse which is still SO far from the actual reality of where data engineering sits today

Am I missing something or is the hype train just really loud right now?

I'm doing a live with Karan Dwivedi to talk about a cybersecurity boot camp that is launching on January 9th! 

This boot camp has an amazing capstone project where you do all of these on a company server!
- threat modeling
- detection engineering
- preventative controls 

Don't miss this webinar tomorrow!

January 9th Cybersecurity Boot Camp Webinar

Daliana Liu

I'm a career coach and entrepreneur. Previously, I was a Senior Data Scientist at Amazon with 7 years of experience in data science & ML. 

When I started my career, I felt lost. I struggled to meet expectations and feared I wasn't good enough. I had to figure out how to navigate my career and advocate for myself. Eventually, I got promoted to a senior data scientist at Amazon, leading high-impact projects and mentoring data scientists and MLEs. My career advice has been featured on Business Insider, Amazon Science Blog, and VentureBeat.

In my coaching program, I help data science/ML professionals advance their careers and future-proof themselves without the burnout. 

If you want to explore coaching, join my newsletter https://dalianaliu.kit.com/profile

90% of data scientists get mislead when reading job posts.

Ignore the exciting vision in the first 2-3 paragraphs. That's marketing. Instead, read the "must have skills" section. That's reality.

- If a "data scientist" role lists Big Query, Snowflake, Airflow, dbt — you know it's data engineering heavy.
- If it lists "LLM" and "AI" but focuses on data cleaning, exploratory analysis, and API calls — you know you're supporting ML engineers, not building models yourself.
(Those are not bad roles, but you need to know what you are getting into.)

The tools tell you what the role actually is. Not the job title. 

The tech stack reveals:
- What you'll spend 80% of your time doing
- Whether you'll work with models or just wrangle data
- If you'll build systems or support others who do

Don't fantasize about the picture they paint.

Focus on the tools. Piece together what the role actually does. Then decide if that's the job you want.

Helping tech leaders and senior ICs gain visibility, build optionality, and achieve their versions of success | Ex-Amazon Sr. Data Scientist

Today, many data scientists are being pushed into AI engineering work because of team requests or company priorities. But they miss doing analytics and ML modeling: the work they actually enjoy.

Some want to find a new job. Others are looking for ways to do modeling work on the side.

Here's the irony: while experienced data scientists are stuck doing AI 
engineering they don't want, people trying to break into AI engineering struggle to get real project experience. And recruiters don't value pet projects from online courses.

So if you're doing AI engineering work you didn't choose and don't enjoy, here's a reframe:

See this as an opportunity to learn a valuable skill without paying tuition. You're getting paid to build expertise that many people are desperately trying to gain.

While you're at it, think strategically: How can you do this work in a way that translates to a high-impact AI project on your resume? One that shows you delivered production systems, not just prototypes.

Even if you want to focus on analytics and ML modeling in your next role, AI engineering skills aren't going anywhere. Why not use this experience to your advantage? Let it differentiate you in the job market instead of resenting it.

There's always "boring" work in any job, even the dream job. If you can't avoid it, turn it into an investment in your future. Add curiosity. Find the interesting parts. Make it count.

You're going to do the work anyway. Might as well make it work for you.

I send weekly strategies on building agency and visibility in your career to my exclusive community, join here: https://lnkd.in/gc3GNK-H

How to get into the AI industry (as a data scientist):

I get asked a lot lately, and here is my answer: there's no such thing as the "AI industry." AI is a tool to solve problems through automation. It's not an industry.

Many "AI startups" are just Silicon Valley tech people building productivity tools for each other. The real AI opportunities? They're in the traditional industry you are already in: Healthcare. Supply chain. Finance. Manufacturing.

These industries are full of unsolved problems that AI could actually fix.
Here's what to do instead:
- Identify roadblocks in your current team. 
- Figure out if AI can remove those challenges. 
- Implement AI solutions in your actual job.

Now you have real AI experience, not just pet projects from online courses.

Stop obsessing over the title. Everyone will use AI in their work. You're probably already working "in AI" without the title. If you still want to move to a tech company that develop AI platforms:
- Find a role similar to your current one. 
- Leverage your previous experience to get in. 
- Then find a team working with AI engineers and learn from them.

Use your industry experience as a strength, not a weakness.

Companies like Google and Amazon have healthcare or finance products. Become a data scientist there first, then transition to more AI-focused roles.

When you want to transition your career, don't start from scratch.
Use your current role to learn with real-world data. Use your previous experience as a stepping stone.

You're not an imposter. You're someone with domain expertise learning new tools. That's valuable.

I send strategic insights on senior-level career growth to my exclusive community every week. Join here:https://lnkd.in/gPFu8fGH

90% people get stuck in writing on LinkedIn because they worry they won't be useful. Here's the shift: Stop creating for others. Start creating for yourself.
Focus on expression in 3 ways:
• What do you want to remember about your career 10 years from now?
• What do you need to get off your chest?
• What do you want to rant about?

Expression > Utility

And here's what always happens: it helps or inspires someone anyway. Because the thing you create purely for yourself might be your best work — because it's honest. 

What moves one person will move many.

It goes both ways. Always give context, don’t do “cold” ask. Don’t create unnecessary stress, also don’t let the real priority get ignored because of lack of “why”.

Founder @ Limelight | B2B Influencer Marketplace

Five years ago, an intern I hired pulled me aside and said, “Can I talk to you privately?”

We stepped into the office kitchen.
She looked nervous.

Which instantly made me nervous! 😅

I said: "What's up?"

She took a breath and said:

“Last Friday… you messaged me saying ‘Can we check in on Monday?’ And then you never replied to my follow-up asking what it was about.”

Context:

I didn't see the follow-up.
I'd already logged off and gone home.

But my lack of explanation created a full 48 hours of stress for someone who was trying their absolute best.

Being new to the business, I get why.

In her mind, she'd screwed up and was gonna get fired.

I never even considered that possibility.

Until she politely raised it with me.

Since that day?

I've never sent a context-less message.

If I write: “Let’s chat Monday”
I always add: “Nothing bad. Want to run an idea by you.”

If it’s urgent, I call. 
If it’s not, I schedule it. 
If it can wait, I leave it till Monday.

To any managers out there…

Please learn from my mistake.

Don’t leave people hanging on a Friday PM.

Everyone deserves a peaceful, stress-free weekend.

Last weekend, I did a 2-day "2026 Kick Off" event to review our 2025, create a vision for 2026, and actually put in the work to start projects. It's like a party+meditation+coaching sessions all mixed together, and everyone left feeling energized with momentum. 

If you want to join this vibrant group to work on your career goals with me in 2026, send me a message.

Senior Data Scientist | Co-Founder of Echoes in AI | Women in Data - SF/ Bay Area Chapter

2025 Didn't Feel Special - Until I Really Looked at It

I was counting down the days for 2025 to end. Honestly, it felt like I hadn't done much of anything. Not a great year.

Then I attended Daliana's workshop, and everything changed.

She had us reflect on the past year—our wins, highlights, and fears we had faced. As I started writing, something hit me. In just 365 days, I'd come so much further than I thought. I'd done things I never imagined I could.

The year I thought I hadn't accomplished much was actually full of quiet growth I hadn't noticed.

Now I'm stepping into 2026 feeling grateful, energized, and ready for what's next 💙

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Zach Wilson

Daliana Liu

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The Battle

Zach Wilson

Daliana Liu

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Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Zach Wilson's LinkedIn Performance

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