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)

How could anyone start if even “junior” jobs want you to already be senior?

I keep seeing “junior” roles asking for:
- 3+ years of SQL & Python 
- A/B testing knowledge
- Experience managing stakeholders

That’s not junior.
That’s companies filtering candidates.

They wanna  pay less for more experience.
And because the market is benefiting them.

 They set the rules.
 And juniors - the least experienced - get stuck.

But here’s my recommendation:

If you’ve got strong SQL, good business sense, and can communicate insights clearly—you are enough.

Don’t let job descriptions fool you into thinking otherwise.

Here are few resources to help you build these skills:
📕 Data Workshop: https://luma.com/7gfx5ss9
📘 Data Roadmap: https://lnkd.in/eYpSaKMx

———

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Amney Mounir

Being a Data Analyst is more than SQL and Analysis.

We are in an era, where AI will help us be more efficient.
We will pull data faster and uncover more insights.

The key is spending  energy on more productive things:
- Uncovering more insights and faster
- Deciding what we should build next
- Convincing shareholders 
- Scaling impact 

———

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Product Analytics @ Meta | 40k+ Followers

Saying “I’m learning ML” won’t get you hired.

Showing what you’ve built will.

Especially with code, models, and results.

Here are 5 projects to make your portfolio stand out as a Data Scientist or ML Engineer: 👇

1. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭 𝐂𝐡𝐮𝐫𝐧 𝐰𝐢𝐭𝐡 𝐗𝐆𝐁𝐨𝐨𝐬𝐭 

Use customer data to predict churn.
Clean data → feature engineering → tune your model.

Link: https://lnkd.in/eDcHXdy2

2. 𝐑𝐞𝐚𝐥 𝐀/𝐁 𝐓𝐞𝐬𝐭 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧

Run a simulated or real test.
Use t-tests or Bayesian stats to back up your insights.
Explain trade-offs clearly.

Link: https://lnkd.in/eZ2ZDr7z

3. 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 

Use Prophet or ARIMA to forecast revenue.
Clean the trend + seasonality. Plot actual vs. predicted.

Link: https://lnkd.in/ePFvMKkx

4. 𝐍𝐋𝐏 𝐒𝐞𝐧𝐭𝐢𝐦𝐞𝐧𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 

Scrape reviews. Clean and vectorize text.
Train a classifier (LogReg or BERT).

Link: https://lnkd.in/e4ncGEf5

5. 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐞𝐫 𝐒𝐲𝐬𝐭𝐞𝐦 (𝐀𝐢𝐫𝐛𝐧𝐛 𝐒𝐭𝐲𝐥𝐞)

Use collaborative filtering or content-based approach.
Matrix factorization, similarity scores, etc.

Link: https://lnkd.in/etiyDarf

———

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Product Analytics @ Meta | 39k+ Followers

How I would prep for interviews — from Junior to Staff:

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

Here are some resources to get you started:
📙 SQL Interview Questions: https://lnkd.in/e5ppwMSx
📘 Case Study Questions: https://lnkd.in/eFKrxkTy
📕 Behavioral Questions: https://lnkd.in/eQ-dqFb2
———

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I don’t care if you have 100k+ followers — I care if your content is actually valuable.

Here’s my list of must-follow Data creators 👇:

SQL:
1. Venkata Naga Sai Kumar Bysani
2. Dawn Choo
3. Walter Shields

Business Sense:
1. Emma Ding
2. Mark Eltsefon
3. Sundar Swaminathan

Behavioral:
1. Jeff Su
2. Sidwyn Koh
3. Jaret André

Story-telling:
1. Morgan Depenbusch,
2. Christopher Chin
3. Gilbert Eijkelenboom

Trust me — if you’re in Data and not following them, you’re missing out!

🎙️ Comment, if I missed anyone that should be in this list

Here is my roadmap to master SQL in 14 days:

When I first started learning, I got stuck 
Because I didn't know where to start
What I needed was a clear path.

So here’s my 14-day roadmap I recommend:

Days 1–4: Build your SQL Foundations

- Write your first queries: SELECT, FROM, WHERE
- Filter with LIKE, IN, BETWEEN, IS NULL
- Use ORDER BY, LIMIT, OFFSET
- Use Aggregate functions

Days 5–7: Learn to use JOINS

- Start by learning INNER and LEFT JOINs
- Then go for FULL OUTER, SELF JOINS
- As a Bonus, learn CROSS joins

Days 8-13: Level up

- Use CASE statements
- CTEs and Subqueries
- Window Functions
- Text Analysis

Day 14 and beyond: Practice!

📗 Data Workshop: https://luma.com/7gfx5ss9
📕 SQL Questions: https://lnkd.in/e5ppwMSx

———

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Here are the 5 things I'd master to become a Data Analyst:

1/ SQL

- Ranking Functions (ROW_NUMBER, RANK, DENSE_RANK)
- Window Functions (OVER, PARTITION BY)
- Basic Functions (SUM, AVG, MIN, MAX)
- Joins (LEFT, RIGHT, INNER, SELF)
- Text Analysis (REGEX)
- Percentile (NTILE)

2/ Business Sense

- Measuring Success
- Improving a feature
- Troubleshooting
- Sizing

3/ Communication

- Using the STAR framework to present your projects
- Storytelling (Executive summary/Presentations)

4/ Stats

- Probability (Bayes, Conditional, Central Limit)
- A/B Testing (p-value, sample size, power)

5/ Python
- Using Dataframe with Pandas

Here are my top resources to get you started:
📕 Data Workshop: https://lu.ma/7gfx5ss9
📘 Data Roadmap: https://lnkd.in/eSCDkmAU

———

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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:
📘 Free Workshop: https://lu.ma/7gfx5ss9
📕 Data Roadmap: https://lnkd.in/eYpSaKMx

———
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We are not SQL developers.
We are Data Analysts.

Yes—writing clean, fast queries is important.

But obsessing over it makes no sense.

Your job isn’t just to optimize code.
It is to help the business make better decisions.

So unless your query is taking too long to run, 
I'd recommend to focus on what matters:

👉 Are you answering the right question?
👉 Did your analysis lead to action?
👉 Did you influence a decision?

———

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You don’t need 30 chart types to tell a story.

These 5 can get for you most of the job done:

1) Bar Chart – Compare categories fast
2) Line Chart – Show trends over time
3) Sankey Diagram – Visualize funnel drop-offs
4) Box Plot – Spot outliers 
5) Cohort Chart – Track retention over time

———

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This is my Data Analyst Starter Pack.
What would you add?

If I had to start from scratch in 2025, here’s exactly what I would do to land my first data job:

𝗦𝘁𝗲𝗽 𝟭: 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬

- Build strong SQL skills (and some Python)
- Improve your business & product sense
- Practice behavioral questions

𝗦𝘁𝗲𝗽 2: 𝗔𝗽𝗽𝗹𝘆, 𝗔𝗽𝗽𝗹𝘆, 𝗔𝗽𝗽𝗹𝘆!

- Make your Resume outstanding
- Build an Application tracker
- Reach out to friend for referral or networking
- Cold outreach potentially hiring managers

𝗦𝘁𝗲𝗽 3: 𝐁𝐮𝐢𝐥𝐝 𝐲𝐨𝐮𝐫 𝐍𝐞𝐭𝐰𝐨𝐫𝐤

- Be active on LinkedIn by posting your projects
- Follow data experts and learn from them
- Have a mentor to guide you in your journey

𝗦𝘁𝗲𝗽 𝟒: 𝐌𝐚𝐤𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬

- Making 3-5 solid projects using free datasets
- Sharing them to the world on LinkedIn

Btw, Sai and I are hosting a webinar on "𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬", you can register here: https://luma.com/ufxt2yfr

———

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When I interviewed at Meta, SQL didn't get me the job.

It mattered — yes.
But everyone was good at it.

What actually make Data Analysts stand out:

- They have a great business sense 
- They know how to communicate insights
- They are very proactive (not told what to do) 
 - They had the mindset by always pushing for more.

That’s the difference between average and great.

———
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This is INSANE! 

GPT-5 looks way too good.

Game-changing. 
Revolutionary.

You will not become a Data Analyst in 30 days.

You will not master SQL and Business Sense  in 30 days.

You will not join a team and know how to frame a messy problem, set the right metrics, and drive the team’s decision in 30 days.

Because in 30 days, you’re just getting started.

Real growth takes patience, mistakes and iterations.

So stop rushing it and build strong foundations first.

Here are few resources to get started:
📘 Analyst Workshop: https://lu.ma/7gfx5ss9
📕 Analyst Roadmap: https://lnkd.in/eYpSaKMx

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Real life data can be very messy 😱

You usually don't deal with perfect datasets, clean, with perfect correlations.

You need to remove the noise and all these outliers to make sense of your data.

And it all comes to becoming proficient in SQL, Python and statistics. 

If you’re interested to learn, check out these top resources:

📕 Data Workshop: https://lu.ma/7gfx5ss9
📘 Analyst Roadmap: https://lnkd.in/eSCDkmAU

———
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If you're just getting started with SQL, this is the only cheatsheet YOU need:

SQL always sounded overwhelming to me.
So many functions.
And different joins.

But when you break it down, it's not that hard:

1) Count & Aggregation → SUM, AVG, MIN, MAX
2) Filtering & Operators → WHERE, LIKE, IS NULL
3) Grouping → GROUP BY, HAVING
4) Joins → INNER, LEFT, RIGHT, FULL
5) Window Functions →  RANK, DENSE_RANK
6) String & Date Functions → LENGTH, CURRENT_DATE
7) Logical expressions → AND, OR, NOT, CASE WHEN

Knowing these can get you 80% ahead of everyone.

So start with the basics and then start practicing.
This is how you will become master at it!

———

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If you're preparing for your next Data Interview,

I made a list of the most essentials pandas functions:

There are 4 common uses of pandas: 

1/ Import and Export Data
2/ Transforming Data
3/ Cleaning Data 
4/ Statistics

Getting to know all of them will be a great start.
Start applying them for your next projects!

Here are additional resources to get your hands dirty:
📗 Python Questions: https://lnkd.in/e_7BaKeC

———
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I began my self-taught journey in Data 8 years ago.

Here’s how my journey unfolded: 

- 𝟐𝟎𝟏𝟖: Completed a SQL bootcamp course on Udemy 
- 𝟐𝟎𝟏𝟗: Became a Growth Analyst at Tophatter (a small company where my manager gave me a chance)
- 𝟐𝟎𝟐𝟏: Joined Cultivate as the first Product Analyst and got laid off from re-org 3 months later
- 𝟐𝟎𝟐𝟏: Moved to  Poshmark as a Senior Product Analyst, then got promoted 2 years later to Lead Data Analyst 
 
- 𝟐𝟎𝟐𝟒: Joined Meta as a Product Growth Analyst 

And the journey continues! 

One thing I’ve learned: you can achieve anything with focus and consistency — even learning online, without a master degree in the field. 

If I were starting over, I’d focus on 3 key areas: 

𝟏. 𝐌𝐚𝐬𝐭𝐞𝐫 𝐒𝐐𝐋 and get really good at it 
𝟐. 𝐁𝐮𝐢𝐥𝐝 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐚𝐧𝐝 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐬𝐞𝐧𝐬𝐞 through mocks
𝟑. 𝐄𝐧𝐡𝐚𝐧𝐜𝐞 your 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐬𝐤𝐢𝐥𝐥𝐬

Keep learning and growing 💪

👋 Follow me, Amney Mounir for more Data tips!

Joins are the most asked questions in SQL interviews.

Here is all you need to know:

- INNER JOIN
- LEFT JOIN
- SELF JOIN
- CROSS JOIN

If you wanna dive deeper, here are my top resources:
📕 SQL Course: https://lnkd.in/eGWcaYxK
📗 Joins Questions: https://lnkd.in/ebmrCDAJ

———

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