Product Growth Analyst

Poshmark

Lead Product Analyst

➡️ Collaborate closely with Product Managers at Poshmark to improve Seller Experience & Live Show Launch & Features:
✔️ Ran 30+ Experiments on the Seller Experience side (A/B and Pre-post analysis)
✔️ 25% successful experiments that led to an increase of 3-4% of GMV
✔️ Led the implementation of mweb listings on the platform, increasing the number of listers by 18k/month, 0.3% of total listers.
✔️ Led Live Shows Launch and GTM effort working cross functionality with data eng and executives

Senior Product Analyst

Tophatter

➡️  Perform detailed analyses to distill key insights & drivers for retaining the customer base
✔️ Designed & ran more than 50+ experiments to measure the impact of new features to improve buyer and seller retention
✔️ Led, designed and built a set of solutions to address buyer retention, including usage of credits and personalized push notification that improve attendees retention by 25% and payer retention by 15%

➡️  Improve platform health with automation in User Acquisition and Supply 
✔️ ROAS optimization for multiple channels spend allocation resulting in a 50% increase in D14 and D28 ROAS
✔️ Improve Seller Funnel from Acquisition to Seller Retention that resulted in a 100% increase in retention and 35% in LTV (Spend per seller)

Growth Analyst

I love Data. And I love writing. I love writing about Data.
Open for brand collab.

(still figuring out the rest)

SQL is 𝘴𝘵𝘪𝘭𝘭 the king of data.

People keep saying “SQL is dead" but the reality is:

- Every interview still has a SQL round
- SQL is still the main tool to pull data
- SQL is still the most reliable language
- SQL isn’t a “nice-to-have", it's a must

Here are few resources for you to get started:
📕 Interview Book: https://lnkd.in/ei8Sp6Qw
📘 Roadmap: https://lnkd.in/eSCDkmAU

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Amney Mounir

When I was trying to break into data,

I didn’t have mentors. I didn’t know what to study.
And honestly… I spent months feeling completely lost.

Doing projects helped me a lot to build my skillsets.

Here are in my opinion the best Github repos in 2025:

1) awesome-data-analysis

𝗟𝗶𝗻𝗸: https://lnkd.in/eVMPj3Nh
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻: A comprehensive list of SQL, Python, ML, and analytics resources — a perfect starting point for anyone building a full data foundation.

2) data-scientist-hanbook

𝗟𝗶𝗻𝗸: https://lnkd.in/eMzixkG3
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻: A curated, minimalist handbook from Andres that tells you exactly what to learn (and what to skip) for modern data roles.

3) cookiecutter-data-science

𝗟𝗶𝗻𝗸: https://lnkd.in/eBdvtNkE
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻: An industry-standard project template to structure your analytics and ML projects the same way professionals do.

4) data-science-template

𝗟𝗶𝗻𝗸: https://lnkd.in/erbER7XP
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻: A clean, reproducible project setup that teaches you how to build maintainable, production-ready data projects.

5) ML-From-Scratch

𝗟𝗶𝗻𝗸: https://lnkd.in/excyjzXn
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻: Clear, from-scratch implementations of key ML algorithms — perfect for understanding models and explaining them in interviews.

———

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What it feels to be a Data Analyst in 2025:

People will only show up when you get the job. 

Not when you’re stuck job hunting.

This process is hard, and you feel lonely, you're trying to stay motivated, trying not to take things personally, trying not to give up. 

Here’s what I wish someone told me:

- Rejection doesn’t mean you’re not good enough. 
- Your "yes" is coming soon
- Stay consistent

And I'm sure, 𝗬𝗢𝗨 will get there!

———

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SQL gives you the kind of power money can’t buy.

Here a few topics that you need to know to get there:
↳ Basic Queries (SELECT, WHERE)
↳ Aggregations (COUNT, AVG, MIN, MAX)
↳ Grouping (GROUP BY, HAVING)
↳ Subqueries & CTEs
↳ Joins (LEFT, SELF, CROSS)
↳ Ranking Functions (ROW_NUMBER, RANK, DENSE_RANK)
↳ String Functions (CONCATENATE, LENGTH, SUBSTRING)
↳ Date Functions (CURRENT_DATE, INTERVAL)

Get your hands dirty with these resources:
📙 SQL Interview Questions: https://lnkd.in/eHEb4u3P
📕 SQL Course Advanced: https://lnkd.in/eAwd6q9A

———

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Most aspiring Data Analysts stop at learning hard skills.

And that’s exactly why they get stuck.

They'll learn SQL, Python, Excel, Tableau...

But this is the part nobody talks about:

Being a great Data Analyst requires TWO skillsets.
And most people only build one.

Great Analysts can:

1) Solve open-ended, messy problems
2) Drive decisions from ideation → execution
3) Communicate insights people understand
4) Collaborate and build strong relationships

Don't get me wrong, I want you to master hard skills.
But invest twice as much in your soft skills!

That’s what will make you stand out in 2025.

———

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Here is how I would prioritize my time to prepare for my next Data Analyst interview:

Junior (0-2 years)
- 70% SQL
- 20% Case Study
- 10% Behavioral

Mid-Level (2-4 years)
- 50% SQL
- 30% Case Study
- 20% Behavioral

Senior (5-8 years)
- 50% Case Study
- 30% Behavioral
- 20% SQL

Staff (8+ years)
- 50% Behavioral
- 40% Case Study
- 10% SQL

If you still don't know how to build your business sense, I highly recommend this free event from Leland, this will def set you apart if you start building this skill!

📕 Register here:  https://lnkd.in/eCgvyvnG

———

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li_at

JSESSIONID

Leland

Data Analytics & AI @ Meta

What differentiates Junior and Staff Data Analysts?

It’s mostly 3 things:

- Scope
- Influence
- Ownership

➡️ Junior Data Analyst:

- Works on small, well-defined problems
- Primarily executes tasks given to them
- Doesn’t influence decisions much

➡️ Senior Data Analyst:

- Owns a medium scope of work
- Have an opinion and influence discussions
- Shape direction rather than just following orders

➡️ Staff Data Analyst:

- Operates at a high scope, often across multiple teams
- Works as a consultant, delegates, and drives decisions
- Recognizes when things are off track and proactively corrects course

Where do you stand? 👇

———
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Every Data Analyst hates waiting for their SQL queries.

If you understand how your SQL is executed,
you can write smarter and faster queries.

Here is how it actually runs:
1/ FROM
2/ WHERE
3/ GROUP BY
4/ HAVING
5/ SELECT
6/ ORDER BY
7/ LIMIT 

When I write my SQL, I always focus on 2 things:
- Making my JOINS less expensive
- Filtering early with the WHERE clause
- Start with the largest table first 

This is where you unlock most of your query speed.

Here are my favorite resource to help you out:
📘 SQL Questions: https://lnkd.in/e5ppwMSx
📕 Data Roadmap: https://lnkd.in/eYpSaKMx

———
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Most people know 𝗼𝗻𝗲 𝘄𝗮𝘆 to remove duplicates in SQL.

During interviews, they will usually ask if you could do it differently

Here are my 4 ways to do it:

1) Using HAVING
2) Using Ranking Functions
3) Using SELF JOIN (𝘭𝘦𝘢𝘴𝘵 𝘦𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘵)
4) Using DELETE (𝘺𝘰𝘶 𝘯𝘦𝘦𝘥 𝘵𝘰 𝘣𝘦 100% 𝘴𝘶𝘳𝘦)

If you wanna practice, here are my top resources:

📘 SQL Questions: https://lnkd.in/e5ppwMSx
📕 The Interview Book: https://lnkd.in/ei8Sp6Qw

———

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💳 Credits to José Siles

I got promoted as a Data Analyst because I asked for it.

I got involved in more projects because I asked for it.
I got doors to open for me because I asked for it.

Don't be afraid to 𝗮𝘀𝗸.
Even if this is scary.

This will make you grow.

It can feel uncomfortable but it's 100% worth it.

———

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A Master’s degree isn’t a golden ticket anymore.

“Do I really need one to get a data job?”
“Will it help me break into tech?”
“Is it worth all these debts?”

Before, having a Master’s 𝘸𝘢𝘴 an advantage.
But, today it's 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹.

You don’t need more degrees.

You need more skills:
- SQL
- Story-telling
- Product Sense
- Real Experience

That’s what gets you hired.
𝘕𝘖𝘛 a piece of paper that cost $60,000.

If you’re going to do it, do it for the right reasons:
→ immigration
→ networking
→ career break

But don’t do it because you think it 𝘨𝘶𝘢𝘳𝘢𝘯𝘵𝘦𝘦𝘴 a job.

Those days are gone.

———

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This is all you need to know in Excel as a Data Analyst:

This is from my experience the things I've used in Google Sheets.

I never used Macros or other fancy stuffs.

If you are able to know these 7 things you're good to go:
- Pivot Tables
- Math Functions
- VLOOKUP
- Index Matching
- Charts
- Logical Functions
- HLOOKUP

Here are my favorite resource to get you ready:
📕 Data Book: https://lnkd.in/ei8Sp6Qw

———

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SQL interviews don’t 𝘫𝘶𝘴𝘵 test you on 1 method.

They test how many ways you could solve a problem

Here are my suggested 3 approaches:
𝘐𝘧 𝘺𝘰𝘶 𝘸𝘢𝘯𝘵 𝘵𝘰 𝘤𝘢𝘭𝘤𝘶𝘭𝘢𝘵𝘦 𝘳𝘶𝘯𝘯𝘪𝘯𝘨 𝘵𝘰𝘵𝘢𝘭𝘴

1) Window functions - fastest and most common
2) Subqueries - works but slower
3) Self joins - great to evaluate your use of Joins

Make sure to save this!

If you want to take it to the next level:
📘 SQL Questions: https://lnkd.in/e5ppwMSx
📕 The Interview Book: https://lnkd.in/ei8Sp6Qw

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