148: How This High School Drop Out Became a $500k Data Analyst (Sundas Khalid)

If you're breaking into data right now, you've probably seen one of Sundus Khalid's videos with over 250, 000 subscribers on both YouTube and Instagram and absolutely killer content. She's near impossible to miss. She's worked as a data analyst, a data engineer, and a data scientist. At both Amazon and Google. But in today's episode, you're going to hear Sundance's story in a whole new light. You see, you know Sundance as the rock star at Google and Amazon that she is. But she's actually an immigrant high school dropout who didn't even speak English until later in life. She didn't go to an Ivy League school, and she even started her career later than most. She is living proof that it's never too late to break into data, no matter your background. So coming up, you'll hear Sundas number one data skill that you need to learn no matter what.

I would have to pick a coding language, and it's gonna be

s t. If she likes being a data analyst, a data scientist, or a data engineer more. I

don't want to pick. I would say like d k is my favorite for building. And

her crazy financial journey and what you can take from it. Nobody

keeps. That much money in their bank account. Like people invest with

that. Let's get into the episode. Sundance. I'm so excited to have you on because you have such a unique story. You're a high school dropout, immigrant child, and now you're an analytics lead at freaking Google. So how on earth did you get here?

First of all, thank you so much, Avery, for having me on your podcast. Um, and thanks for a great intro. It's a long story, but I think like you summarize it really, really well. I am a high school dropout and I'm an immigrant and I was six years gap between my high school and my university. So when I look back now to like 10, 15 years ago, I can't believe that I am here. So it's been a long journey, a little bumpy, but I am really grateful for all the support that I've had throughout my. career and throughout my education journey. So a TLDR is that I went to University of Washington, went to business school, and in the business school, I actually learned about data analytics, databases, SQL and whatnot. And that's where my love and my passion started for the data field. Then I just kept building on top of it. I didn't have enough time to graduate with a CS or a data science degree. So I ended up building on my own, like continuing learning on my own. So I'm a self taught data engineer, data scientist. Data analysts, like whatever you want to call. So it's been a long winded journey, but I'm so happy to be here and I'm happy to answer and deep dive into any of these topics, uh, you'll let me know.

Well, I'm super excited because I think there's a lot of people who are watching this, who are like you, who might be immigrants to the U S who, you know, maybe started school a little bit later or later in their career. And they're like, man, I don't know how the heck I'm going to break into data analytics. I think you're living proof that like you can start late. You can start disadvantaged. Like you didn't even start speaking English till later in life. And you can still end up on the top, which I think is really cool. And also you didn't go to like a brand name university. You didn't study computer science. You didn't study math. You kind of just studied business. Uh, what's been like the, the biggest thing for you in your career journey? That's allowed you to, to get to where you're at.

Um, so I think like a couple of things that helped me really daily, uh, in my career. One is. Knowing what I want to do and when I don't know what I want to do, like I still kept going. So when I started in my career, like my first internship, what am I was at Amazon? Um, I was lucky enough to get that internship. It was really coincidental because I learned about that internship at a networking event while I was going to school. Prior to that, I was getting rejected from all the internships. From my experience, like I have always been open to trying new things. And Amazon is something that I didn't want to try initially, but I just jumped into it. One of my best friends at that time, um, he actually worked at Amazon and he was like, no, you have two kids. There's no way you're ever going to survive at Amazon. I was like, no, I have to try it for myself and I have to go for it. I ended up going for it. And that was ended up being one of the best career decisions that I've made because one, Amazon took a lot of chances on me. Like it let me try out new things, for example. My first job was a data engineer, uh, which I like passed the technical interview screen, but there was still a lot that I needed to learn on the job. So my teammates, my senior members on the team. basically taught me a lot during my first job as a data engineer. Uh, secondly, like one of the advice that I got from my mentor early on is, um, I couldn't figure out, I, I would always meet people and they were, they were always like so passionate about specific topics, specific area. And I wasn't really like passionate, passionate about it. I think I was doing data engineering at that time. So I asked my mentor, like, what should I do? I know I'm not like really passionate about something. In particular, like I like data engineering, but I don't know if I want to do that long term. So his advice to me was sometimes you find what you're passionate about and sometimes you don't. And if you don't know what you're passionate about, you still keep going and eventually you'll figure it out. So that's exactly what I ended up doing. I did data engineering and I found a data scientist role. And that, for me, like, this is, I knew, like, that's the exact next thing that I want to do and I pivoted. Having the right mentors by my side, having the aptitude to like pivot and learn new things has been like really, really, really helpful in my career. And lastly, I, I, I would, I want to say like luck definitely plays a role. You being at the right place, right time definitely puts has some, there is like some luck involved. Like it would be unfair if anybody comes to you and say like, it's all hard work. It's not all hard work. It's hard work you putting in the work, but also like you have to be at the, sometimes you have to be at the right place, right time for things to happen. I like to say,

yeah, I like to say the, the harder you work, the luckier you get a lot of the time. Like, um, if we go back to, you know, landing your first day at a job, you're just a business student. You've taken a few like it classes in, in your college career. But at the end of the day, you're like a business major. Like you said, with two kids, how the heck are you going to start interning at Amazon? Um, and you went to that networking event and I think that's kudos to you because a lot of people wouldn't have gone to that networking event because one, it's like just another thing to do. Two, those networking events, a lot of the times they're very awkward and you have to like go up and like present yourself to people and you're like, hi, I'm Sundance and like, you should hire me and stuff like that. And so yes, like luck had a big part. Like they had to be interested in you at that networking event. Um, but just like the fact that like you showed up, uh, I think is. That's a lot of people don't. And that's, that's the hard thing is it's uncomfortable to show up sometimes. And then the other thing I want to say, uh, about you, Sundance, that I think has really stuck out to me, uh, we've gotten to meet, uh, in person for a couple of days, uh, a year ago, and then we've also just gotten interact online is like, you're a very clear communicator. Um, like you're very good at like knowing what you want to say and making it very easy for the person you're talking with to understand like, okay, this is what's on this means this is like what she's doing and this is what I should do because of it. I think that's played like a huge role in your career as well. Would you agree?

Um, I think that definitely I would agree with that. And I have to give credit to Amazon because, um, Amazon teaches you a way to like communicate in writing and in talking, like they're very direct. When I left Amazon and I went to Google and I was like asking people who were previously at Amazon and now work at Google, I was like, can you give me advice, like, uh, tell me what I need to do differently and their, uh, their advice to me was that like. Be a little less, um, I don't want to say indirect, but like soften, soften up the language a little bit. So like when you say that, like I'm not surprised at all. Like I can be very direct, yeah. Not as direct as I would like to be, but I can be like very direct and crisp and clear in terms of like what I want. And I think that has helped me outside of work, like in content creation and like being on YouTube and, uh, teaching people things. So it's been helpful.

I agree. Yeah. Your YouTube audience, your, your Instagram audience, I think, uh, would agree as well. Now, like you said, you started off kind of in this data analyst role. And then you kind of pivoted to data engineering and then you kind of pivoted to data scientists. And so you've actually worked in like the big three data professions at both Amazon and Google. So I'm actually curious, uh, which of these positions did you enjoy the most?

Um, okay. So I wanted to say You know, it's a tough question. I left data engineering, so like there has to be a reason. I would say like, they're all my favorite for different reasons. I don't want to pick. So I would say like data engineering is my favorite for building. Like you get to build things. And this is like one of the things that I miss about being a data engineer. Like I don't build things. I don't build like data pipelines or platforms that other people use. And I can, at the end of the year, I can be like, Oh my God. These are the number of people that use my product or the pipeline that I use. I think like data analyst has some aspect of it. But like, I definitely miss that from the data engineering point of view. What I don't miss is the on call. So that's definitely another topic. Uh, the data scientist world is amazing. It's just so, so huge in the ambiguity. I kind of like to have love hate kind of like a relationship with like the ambiguity. But I really love that. I can actually take an ambiguous problem and solve it in data science. Uh, when I was working at Amazon as a data scientist, one of my, the ideas that I focused on was A B testing and experimentation. And the coolest thing about A B testing and experimentation is that it would be, like you would run, one of, some of the tests that we would run would be very small difference. For example, you would change the font color from red to blue. And you will see like a huge shift in customer behavior, uh, the purchases, the orders, and so like things like that, that I had previously not thought about, like data science role made me like think about that. So I really like that aspect of it quite a bit, quite a bit. In terms of the data scientist job family, it's humongous. Like you can be more on the machine learning side, more on like the product data scientist side, I would say like my favorite one is definitely product data scientist side, because you get to mix both product. So you kind of like a data scientist times a product manager in one role. So you're able to like, think of more creative ideas and solutions. As a product manager, but then solve them, um, as a data scientist. So I guess like I did pick my favorite.

There you go. It's data scientists. You just had to talk it out, I guess. Yeah. Um, that's very cool. I like that. You talked about like, okay, yeah. Data engineering is building data. Scientist is like more experimenting and trying to figure out how we solve. Real world problems with math. And then data analyst is somewhere, um, in, in between now, obviously in those different roles, you've probably been using different tech stacks, but there's definitely some overlap as well. I'm going to make you choose one again. If you had to choose one tool you've used the most in your career, what tool is it?

Okay. I would have to pick a coding language and it's going to be SQL. And I don't think it's a surprise to anybody listening to this SQL is regardless if you're a data engineer, you're a data scientist or you're a data analyst. You have to learn SQL and you have to. Not even know it, the basics. You actually have to know that vast level if you really want to grow in these roles. In terms of the tools, I would say like each role uses different set of tools and they don't have anything in common. So like, I'll stick with the coding language.

I like it. I think, yeah, maybe that's not a surprise that, uh, SQL, it's like the most in demand data skill in, and honestly, all three job families. It seems like, you know, I think Python gets close for, for data scientists, but It's, it's really SQL. Okay. So SQL is the tool you've used the most. Do you, do you, do you have a tool that you like to use more than, than SQL?

You mean like a coding language or just like coding

language or like Tableau or Looker, or I don't know, like, is there like some tool you really enjoy?

I think the tool that I really, really enjoy is Google Collabs, um, notebooks, uh, because they are like so, uh, dynamic, like you can like code in R, it's like similar to like Jupyter Notebook, but I guess like I never really, really got the hang of Jupyter Notebooks, I've always been like a Google Collab person, so I really love using Google Collab as like part of my job, and what I love about it is like you can write any language, like you can have one notebook and write so many different languages, to produce the results and you can share that code with just literally a link with somebody else that who's going to like take over your work or like scale it and apply it.

That's huge in, in the workplace, because like, like you said, like sometimes maybe you're the data scientist and you're writing the code, but you're not necessarily the person who's going to put it to scale, or maybe you just need to share it with your manager or some other product owner or something like that. Uh, but it's also big for those of you who are listening. Who haven't landed a data job yet, because if you ever do any projects in Python, if you do it like in Jupyter notebook, you're not going to be able to share it very easily and like doing it in Google collab allows you to like have a link that you can send to a recruiter or hiring manager and it just makes like your life easier in terms of sharing the work that you've actually done. You've been at Google for five years now. Um, and, uh, for those of you that have listened to send us on her YouTube channel, um, you've, you've maybe heard some of her stories. Um, I highly suggest checking it out. We'll have a link in the show notes down below. One of the cool things that I think that you've done, and we'll get into negotiation here in a bit. Um, but you, you know, you were at Amazon, you actually used. Like multiple teams at Amazon, not really on purpose, but to kind of compete for you that allowed you to kind of get a little bit more advantageous roles. And then you interviewed in the past at like Microsoft and got offers from Microsoft. And that allowed you to, you know, get some, some better opportunities at places like Google. Um, but you've been at Google for five years now. Um, can you just tell us like what your role is and what do you feel like? Um, like What you do on a day to day basis and what you feel like you've learned.

First of all, like I'm surprised that you watched all those videos because like some of those, some of that information I know lives, like the Microsoft offer lives in our videos somewhere deep, so I'm grateful that you watched that, uh, definitely another story, like how I use my Microsoft offer to like get more from my employer, Amazon at that time, but in like my current role, um, at Google is primarily focused on Google search. Uh, so like when you search on Google, like you'll see. Some ads. So like I work in Google search ads and then there's another tab that you will see like shopping. So like the ads that you see in search and shopping. So that's the part of my, uh, that's part of my team and that's what I support. So my work is primarily like focused on like doing advanced analytics, like experimentation. Uh, deep insights and kind of like figuring out what works and what doesn't. Um, so it's like a, I would say like, it's a, it's a hybrid of data scientists, product data scientists, and advanced data analytics all merged into one. My typical day to day depends on the project that I'm working on. So for example, uh, right now that the project that I'm working on that I told you, like before this call, like. It's a large scale project, and we've been working on it for many, many years, and it's currently in the implementation stage. And while we're implementing, things that could go wrong are going wrong. So, my current project is figuring out, uh, there is a small traffic that we launched, and I'm doing an investigation to understand, like, what exactly is happening. So, like, doing deep dives there to, like, root cause the problem. That's my current focus. Last month, if you ask me what my day looked like, my last month, my day was, um, my days was focused on my, on experimenting. So we were running a lot of like sequential testing. So I was doing a lot of like experiment analysis, trying to understand how different arms of the experiments have performed and what decisions we need to take and whatnot.

Very cool. That's, that sounds very cool. It's so, it's, it's so neat to like hear that. I like it. Oh yeah, there is data scientists working on this product that I literally use every single day, you know, and they're improving the product based off of what I do with the product. So I think, uh, that's, that's really cool. And like for the, for the years that you've been there, like, what do you feel like you've taken away as like your number one, like piece of advice? Like, for instance, if you were to go back. To Sundance five years ago on day one of starting Google, you actually have a video, I think, where you did like the day one of Google or something like that. Uh, if you were to go back and talk to that Sundance, what advice would you give her? And what would you tell her that, that maybe you, you wouldn't have realized or thought back?

So let's go back a bit in terms of like, when I was at Amazon. So Amazon was my first job and I spent about six, seven years. If you like count my internship time as well, my internship was eight months long, which is like not a typical internship time. time. So I always wanted to experience industry outside because Amazon is all I knew. So when I started looking for jobs, like I had a few companies in mind that I was interested in, and Google was one of them. And let me just say that if I hadn't joined Google, or if I hadn't left Amazon, I wouldn't know like what it's like, you know, Experiencing different work cultures and figuring out what I actually like. I think one of the biggest learning for me personally is learning about like what type of culture, work culture and work environment I want to be part of, uh, what I need to look for in my next job. So one of the big things that I immediately learned at Google or like noticed is the culture and how nice people. Uh, for example, like I, my Nugler orientation was in New York. Um, and I was meeting some of my teammates there that I've never met before. And they were like, where are you based? I'm like, I'm, I'm in Seattle. And their response was like, love that. Love that. I'm like in my head, I'm like, why are they saying love that? It's, uh, I've never even met them. And this is the first time I'm meeting them. Maybe just, they're just trying to say that to me. And then. Weeks past, months past, like this was like a normal people behavior. And eventually it kind of like rubbed onto me as well, where I picked up that language. Um, so I would say like the biggest learning for me has been like just seeing how people first culture actually looks like. And I'll talk, I'll definitely talk about like how Google has been an inspiration or has like helped me learn, become financially literate. Because if I hadn't joined Google, I don't think I would. I don't want to say ever, but like, I don't think the chances of me becoming a financially literate person would have happened if I hadn't joined Google. So the number one thing definitely stands out is like the culture and people like, uh, Google has some of the nicest people that I've ever met. And what I like to tell my friends is like a different world inside that everybody's just. Um, really nice to talk to. It's

it's

pleasant. It's always pleasant. Just so, I

mean, I think they give those vibes off like, uh, like the campus seems fun and like playful. I've seen some of like videos and pictures there. And, uh, I mean, like even like the logo feels a little bit, maybe. More playful than other companies. And it is, I think what you said is really important that like, you need to go out there and try different companies, um, and maybe even different industries. Because, uh, what I found in my career is I started my, my data career at a really small biotech startup that had like 15 employees. I love them. Shout out to vapor sense, but like, you should have seen my desk. Like it was, it was kind of like a box basically, like in a closet and uh, like I didn't have nice equipment. And so when I got an offer to go to Exxon mobile, uh, at this huge campus down in Texas, like this awesome sit stand desk, I was like, Hey, I need to try that and see what it was like. And then I got there and I was like. Crap. I hate working for a 70, 000 or not 70, 000, 70, 000 person company, uh, in manufacturing and I would never, I would have never known that. And I'm glad I still did it because I would have always been like, well, what if I like working for a big company that gives me nice perks, but I actually, like when I was there, I realized, crap, I want to go back to like the rag tag team, you know, of like a small company. And then I started my own company. Now I'm a company of one and I like that. Right. So, um, I think it's really cool that like. At the end of the day, like we're all, we're at work for, you know, 40 hours a week, most of us, right? Something like that. Maybe more, maybe less. Like we want to be doing something we actually enjoy with people we enjoy in an environment that we enjoy. And obviously the money is important, but like if, if you paid me a bajillion dollars, okay, maybe not a bajillion, but if you paid me a lot of money to do something I didn't enjoy. A million! Okay, if you paid me a million dollars, but I hate my life, I don't know if I would do it. If you paid me a billion, I'm probably in, but a billion, I don't know.

Listen, you get that million, you work for a year, and then you retire. So.

Perfect. There you go. So obviously one thing that people are really interested in is like this new wave of AI. Do you have any tips on like for people of how they could be using AI at work?

Yeah, um, you know what? That's a great question because AI is like the new hot topic and literally anyone, everyone is talking about it. So if somebody in this world who doesn't know what ChatGPT, Gemini or any of the generative AI tools are, I don't know like who you are, please identify yourself because Literally, everybody knows it and have at least tried once. Um, in terms of like using it at work, I think it's becoming, uh, more and more popular in the workspace. Uh, so some of the things that I have personally done and use AI for is like coding. So let's say if I'm writing a SQL code or a Python code, and I can either, there's like, um, There's an AI built in that can help me like finish the code. I think GitHub AI, what is GitHub's version called?

Copilot is it? Copilot,

yeah, literally basically Copilot and all of these other tools that like helps you finish coding. So like coding is definitely one of the use cases. So if you are a coder, definitely take a look at, look into that. One of the things that I'm really, really proud of is, like, I wrote a document in less than 30 minutes. It's a two page document using Gemini, which turned out to be really good. I did not use the exact copy of the Gemini, just for the reference. Um, I basically got an outline, got some, some sections to fill, and then I turned it into my own language. Sometimes when you stare at a black piece of paper, it's just difficult to start, so having Gemini built in, I'm able to kind of like, have it start, and then I like, I, I basically jump in and like, take over. Then email writing and email summarizing, like sometimes when you have like, long You can literally use email that is built into like Gmail and other email tools to like summarize the large thread and help you understand what exactly it is saying. So it's like a great time saver. The two, the last two that I want to mention is like summarizing Google Slides. Sometimes I get access to like these large decks that I really do not want to go through. So I will just ask Gemini to like summarize these for me. And then my last one, my favorite one so far has been Notebook LM. Um, I don't know if you have tried Notebook LM, but it's literally, it's just, Just mind blowing what it's capable of. You can basically actually did a YouTube video on this where I did a walkthrough. I'm writing my next newsletter is going to be about notebook LM as well, but basically you plug in your documents. You can even link articles, YouTube videos. Um, and you can ask it to like, uh, create summaries, uh, for you. It's basically like your own tiny rag system that you have built using NotebookLM that you can like ask questions that are like specific to the documents that you have imported. It can also create a podcast for you. I mean, I can talk about it for a very long time. I love NotebookLM, like one of the projects that I mentioned earlier, I'm actually using NotebookLM to like scale all the work to global teams. Because notebook LM can literally, I can import like the dozens of documents that I have from last two years, um, and like build it one repository. And instead of like somebody who is like onboarding on this project, instead of like reading through every document, they can just like ask questions to notebook LM and like get an answer, which I think is really, really cool. Okay, I'll tell you one thing. I don't need to use a tool to figure out if you wrote something with chat GPT or Gemini, like I can, I can read your script for like 10 seconds, and I'll know like you wrote something. So recently, we're going off topic, but recently, like, I was interviewing for my personal assistant position and there were like 500 applicants and after reading those 500 applications, like, I kind of figured who wrote with ChatGPT, who wrote with Gemini and like, what are they doing? So, use it at your own risk. But it's a great starting point, but it's not, it shouldn't be your end point. So I won't be watching videos that are just had deputy scripts because I can tell,

I like what you said earlier. It's like a warm start. You're not starting from a blank, blank slate. Um, I know I've been hiring a lot recently and there's been multiple candidates, I would say like close to 10 to 20%. That forgets to like put my name, like it just has like the blank, like brackets that chat GPT gives you. And I'm like, guys, come on. I can't trust you. This is The funny thing

is like all of them were using the same structure, like how in the world you all came together and just use the same structure. Like this section is going to have this, this is going to be this section. It was just crazy. Like, please, if you're like job searching, please don't use like raw chat GPT output, like you're just risking. So your, your application by doing that.

I love it. Okay. Thanks for your AI tips. I appreciate it. Uh, let's talk some more about financial literacy, because you said if you'd never been at Google, you may have never gotten to financial literacy. You cover, you cover a lot in your content, um, which is important, right? Because. As much as you and I love data and everyone else, we probably wouldn't be doing what we're doing right now if it wasn't for the fact that like, we want to be like financially secure. Um, and I love how transparent you've been. You've done like a whole like 10 year salary, um, like documentation of like where you started. It was like something like 40, 000 to like over 500, 000 in the last like 10 years. So what made you like, what was like the thing that made you get into financial literacy?

Yeah, no, that's a great question. Um, I think it all started when I attended this one talk at Google and it's actually on their YouTube. Um, this was by an author called, uh, his name is JL Collins. He wrote The Simple Path to Wealth. The book is really popular now. Uh, but basically he came to one of the Google talks and I ended up attending, which I wasn't planning to. And the way he talks is like, he talks like he is like your uncle and he's like trying to explain you like, what are. What is investment? What you should be investing in? What is a retirement account and whatnot? Ended up buying his book, ended up reading it. And that's where like my, like that, me attending that like 30 minute talk had Google basically inspired me to get into financial literacy. After that, like ended up reading Ramit Sethi's book, I will teach you to be rich. Like these two books combined literally gave me everything that I needed to know. And the funny part is up until this point, like I was in the industry for about six years. At Amazon, I had no idea that Amazon offers 401k match. I never really invested in 401k. I left that 401k match on the table. And I had all the money in my savings account. Like all I knew was savings. So I just kept saving. So every time I went to my bank account, like the bank tellers, the managers would come out and they would be like so nice to me. They were like, why don't you come sit here? I was always wondering like, why are they so nice to me? And after I became financial literate, I realized like they were nice to me because nobody keeps. That much money in their bank account, like people invest anyways. So that led me to like openly talking about financial literacy because there are many people like me who don't fully understand like how to actually make your money work for you. Like, I don't have any like certifications or like, um, what is the accolades to say? Like I'm a financial educator. Like I'm just sharing what I am doing. And that's what I started doing. Like I started sharing like what I'm doing, like, this is what I'm reading right now, this is what I'm investing in right now. And it turns out like I have inspired a lot of people to like, become financially literate, like two books that I mentioned, like I've shared with thousands of people, they have read it too. And eventually, uh, I was like, okay, how can I like make more impact? Because I'm so passionate about this topic. And that's when in like 2021, I decided that, okay, I'm going to like volunteer my time to help other people negotiate their salaries because. Another story, which I cover in detail in my course, but basically when I got my data engineer offer from Amazon, it was way, way, way below. Just to give you an idea, my first year salary was 65, 000, which is for a data engineer role based in Seattle, which is high cost of living. Anyways, that led me to being on a path to figuring out what my market rate is. Eventually when I learned all those things for myself, I wanted to help other people. So then in 2021, I started volunteering my time. If somebody had an offer, like I'll go and basically help them like negotiate their offer and give them strategies and whatnot. Eventually I realized like that is not scalable with a full time job. I cannot just get on a call with everybody, uh, to kind of like give them consulting and whatnot. I'm, I don't know if you've ever done like one on ones, but like those are difficult to scale.

I did 250 last year.

I'm on the death. How do you do that?

Uh, I did, I tried, yeah, it's hard. It's really hard. Yeah, I totally get it.

Yeah. It's hard. And you hit a limit at some point. You were like, okay, there's no way I can do more. So then I was like, okay, I, how can I like continue scaling this? So that's when I ended up building the course that I have right now, which is on salary negotiation, where I like share all the tips and tricks on how somebody, anybody can learn, uh, salary negotiation strategies and skill and can negotiate their own salary. The course that I have is like specifically focused on tech because that's what my specialization is like, I guess my area is, but yeah, happy to talk more about it. I actually have a special discount, a coupon code for your audience. So, um, yeah, and I'll share it with you. You can, it's a, it's Avery20. So like if you go to the website, which you can link here and use the coupon code Avery20 to get 20 percent off.

Okay. Awesome. Yeah. I, you're being humble because, um, like obviously like, like, uh, you, you've been really good at, great at this, but like in one year you helped 50 different women. Negotiate like 1. 4 million of extra incremental salary, not like total salary, incremental salary. Um, and I did the math. I'm pretty sure. I think that's like 30, 000 per person on average. And I just want to like highlight this to, to everyone listening that like some of this is offering this, obviously it's, it's paid, but like. And you might not get 30, 000 out of it, but you're going to get a lot out of it. And the coolest part about salary negotiation, in my opinion, and Sundance, you're the expert here. So correct me if I'm wrong. The majority of the time, the worst thing that happens is they say, no, sorry. Right. And the best thing that happens is they say yes. And the most likely thing is they meet you somewhere in the middle. And so like, in my opinion, correct me if I'm wrong, like there's not really a downside for asking for more money. The majority of the time.

Yeah, like unless until the recruiter says this is the last and final offer, like you, you wait for those words and unless until the recruiter says the last final offer, it is not a final offer, basically when they give you the first offer there is still room, they leave room for negotiation because a lot of people do negotiate even though like there was a study done like 50 percent of the people Don't negotiate, which is surprising, but when the recruiters are giving you that number, they are leaving room for negotiation for you to ask more. And when you accept that and believe me, like I've been there. Uh, when you go through that rigorous job market, like that we are currently in, and then you go through like so many interviews and then you finally get an offer. You're like, thank God I'm so done with this. I'm like, so over it. Like the first offer or like whatever offer number you are on. You get it and you're like, I want to just sign it, lock it, and like be over with it. Just like hold on a little more and just stay patient in that stage. Because chances are it could be just you simply asking the recruiter and they might come back and say like, yes, I can increase your compensation by this much. Don't accept without asking, um, and listen for those words, a last and final offer. Cause, but even with that, like there's a lot of strategies that you can use, for example, like competing offer, uh, the market research tools and whatnot. So there are ways to negotiate, uh, but don't accept your first offer.

I just think this is so cool that you're doing this because, um, I honestly think it's one of the best investments anyone can make because when you've gotten to that point where they're literally saying, okay, we're going to offer you. Like they don't want, they're not, they're not looking to get rid of you. You're looking to hire you. And so if you ask for more money, it's not going to be, they're not going to be like, Oh crap. Like, nope. See you later. You're not getting this job offer. Like, as long as you're like really appropriate and you, you do do it professionally, you're probably going to get something. You might get nothing, but at least you can ask. Um, and the other thing I want to just really highlight, you know, going back to the financial literacy thing. Is, you know, let's say, let's say you negotiate and let's just say you get, let's just make it somewhat smaller. Let's make it 3, 000 instead of 30, 000. You have to realize that that 3, 000 is going to be there every year, the rest of your life for that salary. Um, so it's basically compounding. So like, let's say you negotiate 3, 000, that's an extra 3, 000. Like you might not get that raise for a year, for two years, for three years, you might not get a 3, 000 raise. And so you're getting that up front and that's just going to compound upon every single year, the rest of your life that you're working. So it's, it's almost like you're not doing 3, 000 and I'm really bad at compound interest and, and stuff like that. But like, yeah, 3, 000 is actually like, you know, 20 years down the line worth like something like 50, 000 or something like much, much more.

Right. And the, the, the part about like, ask if you're able to get that 3, 000, for example, let's say your compensation for software was 100K and you end up negotiating and now it's 103K. So let's say next year when it's performance review, like you are given the annual raised and most big, major mid sized companies, you're giving even like the smaller companies, like the, your annual raise is based on your base salary. So. You're going to be given, given up certain percentage, let's say 10 percent raise on that 103 number instead of the a hundred number. So like it does add up eventually.

Yeah. Which I think is incredible. So, um, that's super cool. You're, you're, you're one of the best at it. You know, you've, like you said in, in this video, we'll have a link to it in the description down below. You've gone from like 40, 65, 000 to, you know, over 250, 000 to a lot more money than that. Just in negotiation, you've done different tactics of like, Oh, you've gotten competing offers from Microsoft and, you know, Amazon and all these different things. Um, so I'm really excited about this and I think, uh, people can really learn from it. We'll have a link in the show notes down below and you can use that coupon code. Send us, thank you for giving us to the audience. That is super exciting. Avery 20. Um, and where else can people, people find you.

Oh, my God. I'm everywhere. Um, I was, I'm actually speaking at another, another live webinar later today, and I'm like, trying to figure out which social should I get? So I'm literally everywhere. I'm on YouTube, Instagram, TikTok, LinkedIn. I just started a newsletter this year, uh, on Substack. So it's my full name. You can use Sundas Khalid on Sundas Khalid. Um, LinkedIn on YouTube and then on Instagram and TikTok, you can find me. It's Sundas Khalid, but there's an extra D, so like two Ds. Uh, and you can link them below and then my website, SundasKhalid. com. So if I ever, my accounts, my social media accounts ever shut down, I will still have my website. Yeah, I was going to say like, definitely subscribe to my newsletter because that's something that is new to me and I'm planning to put a lot of work into newsletter this year. So, uh, if you want to hear from me, like in your inbox, like that's the way to go.

Perfect. I look forward to that. Uh, I, I really enjoy, uh, all of a sudden this is social medias, but specifically I think her YouTube videos and her Instagram videos are really great. Well, perfect. We'll have all those links in the show notes down below. Sundas, thank you for coming on.

No, thank you so much for having me, Avery. And I love that we have that blue and red vibe going on. It was perfect. It worked out. So

fun.

Awesome. Thank you, everybody. Bye.