The Data Hustle — Slawomir Tulski

If your job is to take something who like very well defined which somebody else prepared for you, you're cooked. Uh that kind of roller coaster of my my audience would grow fast. I would have leads from LinkedIn exposure that does gives you things but exposure can be taken away. Today we're joined by Slavir Tulski, a data leadership consultant and forum metadata engineering manager who helps organizations identify structural leaks in their people, processes and data stack. His work sits at the intersection of data engineering, leadership and organizational effectiveness. He brings a sharp perspective on what it takes to make data work matter inside organizations. So Slamir, thank you for joining today. We're very happy to have you here. Um can you tell us something about yourself

and your background and what you have done and how did you get here? >> Sure. Sure. Thank you very much for having me here. It's honored to be here with you. So pretty much as as you said I'm saf I actually came from the academia background. So my background is in geoscience. I supposed to be a scientist. So I was uh with my PhD lined up. I was assistant professor doing lecturing at the university and I also work at ML startup doing some some image detection and that was you know back in the days in Poland and I barely could offer living because I don't know if you know but academia is not the most profitable but so at some point I get tired and I

dropped everything I dropped my country home country I moved to the UK and I dropped all the science aspiration or anything and I decided to move to software engineering And that was like what 15 years ago and back in the days. Uh that was you know much easier transition than nowadays and it was the the high the salaries were still on the top. So I was very practical. I was like I I won't be ambitious. I will just make money. Uh you know quickly realized that being software engineer is also ambitious. But long story short from software engineering naturally gravitated towards data engineering because of my background because of my background and yeah worked on a bunch of startups on

that was a surprise for me because when I was moving to meta, I mean Facebook back in the days, I was very heavy on the programming uh DevOps programming you know building data platform and then when I moved to meta actually their approach back in the days I still counts was that they are more like you would call analytics engineers nowadays. So I did not have to do any platform. I did not have to worry about clusters, machines, infrastructure, CI/CD pipelines, anything. All of that solved for you. The only thing they really wanted from you is business impact. And that was a huge shift for me, right? So I felt like I'm good at programming, I'm good at very technical stuff, but I

felt that the communication, soft skills, influence, and actual business part is what I'm lacking. And that was something which I had to push really really hard at meta and uh you know I didn't even realize how much value it will bring me nowadays as a consultant and also like when I I was at the leadership positions as well this kind of focus from you know having fun as an engineer to actually thinking about the business and business term that was something pretty unexpected for me. So that was like the biggest difference. Uh right. Of course you have other differences like this technical stack is different like big companies will have a lot of internal frameworks and all of this. But that's that's

>> h that's work for extension you I don't think you really have to care about business impact when you are hired as a work or extension or contractor to implement something very specific. Now if you want to go one level above that which where the money is that's one thing and also where where where the real value lies it's you indeed have to uh link everything to the value right and the same project can be viewed as completely different thing I will give you the example of another consultant uh they are doing some uh customer segmentation right you could position this as as I'm doing customer segmentation purely I do XY Z like I build pipelines, I build dashboards, I do customer

segmentation or you could frame it as I help a teams to optimize their ads marketing spend and I do it via you know uh customer segmentations and I help to frame the strategy of how to actually meaningfully spend companies money right here is way like that's that's the the revenue outcome but I would say like really if you ask about consultants there is like huge gap between full-time employees and organization and what an organization wants versus your uh you know perspective as a consultant right if you want it's al so that's I would say those are two different considerations here and depending if you're asking what companies should do to stop being a cost center and become revenue driver that's a one question and

Um, it it it's an important question I think and it's even if you are now in this technical consultant role, I think at some point you will need to think about transitioning to the second kind whether

you want it or not. I think so. Yeah, I think I agree with you. And and the way I see this is, you know, we are in this weird period where we try to figure out where the things are. But if your job and and I always keep I'm keep telling this to also to to data engineers, full-time employees. I think this applies equally. If your job is to take something who like very well defined which somebody else prepared for you like a technical specification and I don't know write a DBT model you're cooked right you're like that's what AI and mai is doing right they take something very well specified and then translate it to the very structured output so if you're

a consultant who does this kind of work which is you know I I go there and I implement DBPT models and they are those I I I know some of them right I would say you're in very dangerous position same for the full-time employees they are still out there many many these kind of positions you know where where you just simply do very narrow thing very well defined like I had a client one one of my clients their development pipeline was this product managers talk to data product manager data product manager talks to solution architect. Solution architects consults it with enterprise architect. Enterprise architects get sign off goes back to solution architect and everything is passed to data engineer. Data engineer is implementing

uh you know DBT model and and we live happily ever after. Right? that last I mean like forgetting aside this ridiculous long chain that last engineers the only thing they have to do is you know close their eyes or or just open the technical specification and and implement everything that's a dead uh dead end and those kind of works will be gone. Uh other thing which will be gone for example is if you are more on the data operations. So there are still those teams and also work for extension and contractors which acts more like okay we are support function you're the enterprise you have thousands of pipelines everything can fail all the time. They don't have the full-time employees to just babysit on calls. So

they have this kind of work for extension where they do data operation restart job check if the app still failed try your Monte Carlo whatever this kind of kind of very operational thing that's another thing which will be gone so whenever you have very narrow scope very well defined very narrow and kind of mechanical that's where the huge risk lie and you're 100% correct you either will want to you your margin will go down, your employability will go down and eventually you will be extin ex extinct. Yeah, you will be gone. Uh because yeah, they're going to I mean there's other question if something new will pop out where this kind of workforce will be able to find their place. I

How do you see it evolving with the current landscape? of course with AI and everything around that and where do you think the opportunities lie for consultants to um specialize in and really distinguish themselves other than of course focusing on providing value.

>> Yeah. Yeah. So, so I think that data engineering despite being on the market for like 20 20 years plus they are still quite it's an umbrella term. is like saying you are software engineer but what what front end back end mobile like what the hell are you doing right so data engineer is like anything data related and I think that this slowly starts to clarify and we're going to see distinction between platform kind of folks and analytics engineers and they're going to be two distinctive paths uh at you know explicitly or implicitly but that's how I kind of see the role actually I would say clarifying a little bit and depending where you are in that spectrum there going to be some some things you

uh you have the opportunity so if you're more on the platform side that's going to be extended also by you know all the AI readiness integrations making sure that all the AI runs right AI is not magic you know someone needs to implement the integration someone needs to prepare the data infrastructure need to be there uh you know the all the layers of AI readiness out there like all of that plus agents. All the agents needs to be built, cost needs to be maintained. So they're going to be a lot of work out there. All right. And on the other hand you will have those analytics engineers which right now you know of course that that role also differs but

but it's going to be something like you know very heavily focused role which which focuses on business and solving business problems through the data. Right? So if you're a consultant you will be able to find in both of those buckets work. That's just going to be completely different work. Right? So the example of the uh of the you know the more the platform engineering is you know building some sort of smart AI engines which which you know operates I don't know for customer success or for whatever for operations. Uh that's one one example on the analytics engineers is is what what we just discussed around you know how to find a business problem and solve it through data.

Uh not waiting kind of to to be told what to do. Uh that's how I actually see this right. So I don't think we are going extinct. I mean we going to be there. There's plenty plenty of work. uh I think some things will clarify some roles will be gone uh but there going to be still opportunities but with the you know increased capacities of AI uh we'll just have to find the different levers it's not going to be I can build that data warehouse right it's going to be I can give you data warehouse quickly which going to be costefficient and by the way we're going to solve XY Z with that data warehouse right so Instead of thinking about just you know implement

data warehouse and maybe have a retainer on the on the maintenance you you push it harder and you sell and you you know you are solving the the the real the real issues with the data warehouse you provided because yeah it's just much faster nowadays to implement it and you have all the tools etc right so that's how I kind of see it right >> that makes a lot of sense to me and I think anyone that has been around in technology long enough and has seen some of these waves of course in new technologies and hype cycles coming up has seen similar patterns over time. I mean you talk about data warehouses. I remember you know meeting consultants that were implementing

this we haven't even realized promises of BI how this works is we have a promises >> big data will solve this or that then BI will solve this or that we never even solved this like we were we would be in completely different space if we realized big data promises and BI promises and data science promises right we ending up like doing half of that and then promising more with the new technology. That's the same with the AI right now. Uh but going back to your question about the political depth so the way I understand it is is this hidden accumulated interest that organization will have to eventually pay for all the systematic inefficiencies they have that comes from you know leadership decisions, culture,

operational rhythm. So I I make fun that at the like this is the root cause of all the evil but the the thing is like stuff like technical depth like the day-to-day business inefficiencies they very often stem not from you know lack of technical capabilities of people or or these kind of things but from the very top from the leadership from the alignment from how we operate between the organizations and I can give you a couple of like really good examples, right? Uh there is like a a client and they have like issue the data takes long time to land like there is months for new data points to land. So there is a business stakeholders they come we want

X and they just wait and wait and wait and wait and they deliver right and then the the the leadership says like our data platform says we can do it in two days and my business partners they told me we are doing it in two months. What the hell is that? So I dig deeper. I do the analysis you know I you know I talk to people I do the entire investigation auditing how the people work and I realize that the 90 x% of the time they wait the way it works is they get the business request if the data is not already ingested they need to ingest it from the producer but producer don't care producer is agile they have this is

the engineering department they have sprints >> and you have to rise a ticket so you raise the ticket. They don't have the idea what the hell is that and they wait, you know, cards, scorecards, planning, grooming, whatever they do in their agile uh circus and it's not being picked up. It's always P whatever because if there's no alignment on the leadership level like they get some random request that we need ingest this random thing and they don't care about that, right? But why they don't care? Like business needs this. There is like actual need. So why why no one is talking? Why there is no alignment? Why why they are not doing this? Why the like the the ticket is waiting one

sprint, two sprints, three sprints, suddenly the the the ticket is done. Some engineer out there push in five minutes uh white listed a table or whatever. Two days later the the stakeholder has data. So weeks of m waiting wasted and the time where they could to do action just because the ticket was waiting. But that's a ridiculous problem. It's not a technical problem. It's organizational problem and that should be solved through the you know leadership through the alignment through the many other things. it just happened not to be solved, right? And many times it's easier said than done because then you are getting into all the power struggle within the organization, right? It's not like you are king of the world. You're there in

the data organization and everyone listens to you. Now you are one of the many department there is you know different you know setup in the company. In some companies, you know, depending of course where the data is, the the the management lines and and you know, the the orc chart matters, are they under CFO under are they are they under CTO like all of this may matter and you may end up in the situation where the alignment with the uh producer department is quite hard and that causes a lot of issues. Now what happens later is this every time that such team needs to get something from the producers they struggle right so they're going to start finding workarounds and I've seen

that like I even done that one myself like one of the companies I work for as a data engine back in the days we had the same issues with the devops is hard to get anything from devops they don't like that we run on the different cloud whatever like they want to have the full control over over our cloud but we don't want to give them control because the sudden we are losing our agility so what happened is they would and that was like pretty much political the leadership was not aligned like it's a little bit gossip so I learned over the years that a lot of that was actually characters clashes on the leadership but the devops or head of devops was

intentionally not super helpful for us and so what we it back then we asked to why at least one server and then we you know directed entire traffic anything what whatever we do was going through that server we like practically created one proxy >> which any any traffic was going through and then we would do whatever we want right >> is it good from security is it good from performance is it best no it's not best like that was like single bottleneck and and there there could be some security reasons but but we have to surv survive. So we created certain technical depths and certain inefficiencies just because the leadership between two teams couldn't align. Right? This another example where this kind of political

depth is playing out and directly translate to technical depth and to the inefficiencies. Like if you really go and chat with IC's and with data engineers and ask how they are doing things, you will quickly realize that a lot of things just happens just because of the culture or leadership or expectations. Incentives is another one that directly plays towards what you supposed to do versus what you were rewarded for, right? and and there is a game there like leadership will tell you you have to do one thing but if what they are rewarding is a different thing don't be surprised that people are doing that different thing the example long-term versus short-term like if you if they tell you oh we need to do a

long-term initiatives and all of that but then they reward long short-term wins quick patches dirty work guess who will do who will be doing what right of course they are engineers are not stupid they will do a quick patch And there's nothing they're going to convince them to do otherwise they will hack system around. So all of those are examples of the of this what I call political depth which is this ensemble of you know leadership inefficiencies culture incentives the way company works and or chart things like that. Uh so so so that's the the political depth. I think there was a further part of the question, right? But I'm not sure if I haven't lost that train of thought.

>> Uh, we went over a lot of topics here, but something that I was thinking of as you're talking about this is it sounds to me like what you're describing is it's important to be able to navigate different organizational cultures and there's many flavors and patterns that may evolve over time. as you're indicating through this back and forth misaligned incentives. >> Yeah. >> And you know many other things. One that I've seen a lot and I've also seen you write about this is where >> a lot of data teams have been traditionally seen as like this data ask factory where it's like here's a Slack channel or teams channel or email and we're going to send you 500 data questions so we can answer them as

quickly as possible please. What I've seen over the years with many people many companies they get overwhelmed and they start getting defensive. They say, "Well, okay, this is maybe an hour ask, but I'm gonna do this two weeks later because I don't want to give them the expectation that I can do this in a day cuz they're going to ask for more and more and more." I do feel though that we are at a stage where it's getting increasingly easier to fix a large amount of simple data ask to things like self-service analytics, semantic layers, AI systems. >> So this okay that's okay >> finish the question and we can we can because I I thought that that's so let

me let me quite >> so I I I will put an asterisk. I'm talking about like the simple things, right? like automation things like getting access to a system >> um and things like that, >> right? Which is usually a >> chunk of of of requests, right? >> Um >> there's going to be a pressure though. Stakeholders do see these developments in technology and they expect more and faster and bigger scope and capacity. How do we balance this? So I think this this is interesting perspective. So, so when I was saying the I'm I'm not buying this, I was more thinking about the selfserving uh suddenly offloading uh data engineers or consultant work cuz that that won't happen like self-s serve and and a lot

of new capabilities just allows us to build more but it also gives you more noise >> like now you know nowadays it takes you one year to build 100 dashboards. uh now you will be able to build thousand dashboards and still not do anything with them in one year right and it just will create more mess so so I I was referring to that part 100% agree like that there is like a trivial stuff like stuff which should be trivial which is not like permissions and all of that uh there is a lot of huge potential here to to be to to to for things being speed up but I think what you were asking is more like if if I understand question

correctly or your perspective. You are trying to you're asking me about this that we need to deliver more and faster because there is this development of AI of the tools the things supposed to get easier now we need to deliver more right is that what you're asking for I'm trying to understand >> yeah so in my own experience also talking with clients and stakeholders there is this perception right and whether that is fair or not That's a whole discussion in and of itself. >> But there is always this tension, right? Like how much should we be doing and how much output should we be having and should we protect our time more and push back on timelines. It is a pretty complicated question and

I think it's getting even more complicated now that you see other parts of the business starting to try to have their own workarounds. of let's say a claw code or a centip and they say look I build a dashboard in one day why does it take you two weeks >> yeah so now again I think I I maybe maybe because of my background but I see strong division between life as a full-time employee and life as a consultant >> right because if you are a consultant and and you are being paid for the value delivered for the projects or for for whatever like I would say this higher tier of consulting. >> Mhm. >> You don't really care, right? What you

own interest that a lot of deliver is supposed to be in the little amount of time so I See the the the that solves your problem because the the the stakeholder can come to you and they can tell you like okay we've built that we could build that dashboard for in in five minutes and why why why you want X for that and why it will takes you two two weeks. Well then then do it in five minutes just you know my my you know my quote is is for this week and next week I I don't know if I have the capacity to serve you right and and they going to fail because they're going to build this dashboard in

5 minutes >> and that dashboard will suck >> right or they have like a great uh they have a great infrastructure they have great data foundation and they actually can do it but again it goes back to this is not your client probably right if they can build it in 5 minutes and it's great yeah they did awesome work right that they don't need your help that's usually not the case so I think consultants don't have that problem unless you are work for extension and you are paid by hour and there is a manager on the top of you which says like we're doing migration we have this migration project client expect us to deliver faster right >> unless you're in this zone then That's

not your problem if you're in this zone. Yeah, that's your problem. And that's why we were also saying that they're going to be problematic uh with the race going down and with certain things being automated because yeah, those things supposed to be done faster nowadays. So I think the impact will be felt here and then there is organization full-time employees that's a completely different story and I really feel there is like >> I think one of my learnings was after years I mean like I'm just one year being consultant and all my previous career was being a full-time employee and the there's such a huge difference in approach and in in incentives. It's pretty staggering. Uh I have to say

But when I hear you talk about it this way, it almost sort of sounds like you should be always vying for the project based and impact based uh billing and contracting anyway because if you are hourly, you're always going to have sort of this this battle like, well, I think you can do this faster. I think it's ridiculous you take so much time. I'm paying you for 20. like, you know, like I want you to work this time and you get these misaligned incentives. I agree with you. You're like, "Okay, I'll I'll spend it 20 if you want 20. >> It's not going to be fast." >> Yeah. >> It's not going to be efficient. >> Um >> Yeah. You're Yeah. Yeah. This this is,

you know, in theory from the consultant perspective, >> uh you know, you probably want to be paid by project and by impact like value based pricing. That's probably your your your your thing. There is something in between hourly billing and and project based billing. I would more call it like a prepackaged sale thing. Let's say you are able you are able to prepackage some sort of service inside in in like a bundle and and then you don't sell value but you have a lot of automation and you're going like let's say that you can put me warehouse out of the box in two weeks. >> Mhm. uh and then you can go down with the price and you can say like for any

client I'm I'm building dashboard uh warehouse for I don't know 5k whatever and then I have retainer after that it's it's not value because you don't know how much you're not like really considering how much value this this brings to your user like should I charge xyz now you're saying I have this prepackaged service I can do it for x but it's still not a v hourly pricing because client does not care how much hours you spend. So now it's more like you know service sale kind of package thing and then you have the hourly billing and yeah I would agree hourly building is probably is easier to get and you know that the the high tier value based pricing is definitely way

you. So that's against you. And yeah, you're a contractor, so you're losing all the benefits for the employees. So I would say this is the for me, this is a gray area, and I would really have to look at the numbers. If you ask me, is it good or is it bad? Is it profitable? Like let's let's talk numbers. uh how much you get on full-time employment, how much you get from that, all the benefits, all of that. Uh but there may be this pressure and there may be this tension you're saying on the hourly billing. I I agree and the prices will go down and there is expectation that should be faster >> like and it's like you can't blame

people like they go on the LinkedIn they go on the any industry event and they kind of feel like tomorrow they will be replaced by by AI and then suddenly >> adding a dashboard takes three months it's like what the hell like why we are paying so much for that. So yeah definitely there there there is something like that. >> Talking about pricing and tactics. Let's say we have a media or senior data engineer that wants to go into consulting and break out on their own. How do they get this in a way? I the way I think about closing a contract like that, you have to build the trust and there's many ways to build the trust. It can be a personal

on my content And I I I had single leadership discussion interview with the person but like literally my my lead from that company told me like like I fully trust you. I like that like I know your content I'm following you for like half a year like I want you I just need to keep everything in line. So one more person has which don't know you has to talk to you. But that was that level of a trust for a value based project just purely from expertise on LinkedIn. Right. So I had that I had like since for some time already dead that silence from LinkedIn just because they cut my reach completely. Right. >> So now can you build this expertise this

level of trust? Yeah you definitely can but it takes time. It takes exposure and it takes a lot of tactics out there. Uh so if if there is anyone wanting to switch to consulting, I would lean heavily on the relationships on your personal networks and then recommendations from that and I would start building the the local local online brands. Sorry. And it does help. There is a huge difference. Another example I will give you. I was doing for some time some cold cold outreaches and the the people either didn't reply at all like if they were not within my my core audience and then when it was when I was reaching to someone who I know knows my content and likes sometimes

share you know comments sometimes immediate response immediate discovery call immediate discussion right so building this This net online presence definitely helps. >> It can help you to build trust. You are exposing yourself. You are sharing your thoughts. Of course, I'm assuming here you're writing, you know, solid things and and technical and you're not doing memes, right? >> Uh so I would definitely say you do that. That helps. But uh yeah, there are some some surprising uh consequences and and and if you're on on single platform like LinkedIn for example, you're going to be impacted by by the algorithm and and you can see people moving to to to you know trying to diversify and do other platforms. So yeah, just don't be naive. Don't think

you invest years of your time and effort into building this public persona and this portfolio of assets out there and then suddenly gets cut to onetenth of the effectiveness it's it's pretty brutal. Yeah, >> it is. It is actually it's I I still publish a lot of LinkedIn but that that kind of roller coaster of being you know fastly I I I my my audience would grow fast. as I would have lead from LinkedIn to to suddenly >> uh the state where I really have to work super hard for anyone to even see anything from me despite having pretty much being in the top of the you know I'm not the biggest one but you know looking at the distribution I'm still on

top right >> uh it made me rethink and nowadays I don't look at LinkedIn as a lead generation I looked at it as branding I looked at it you know more through those lens plus personal connections and trying to connect to people on LinkedIn so now you know if I have if I have a discovery call with someone who don't know me I know they will check LinkedIn and they will see all of that >> so I treat it more like this whereas at the really beginning where I had the growth where I have the exposure >> I have people directly coming to me you know from businesses to clients so exposure that does gives you uh things but exposure can be taken away pretty

fast from you as well >> intern fame can be transient as they say >> well that that happens I think one thing which which I'm learning is yeah one have to adjust and be flexible I mean I always knew that from my corporate life >> but it's a little bit different level on the >> on the consulting Right. >> No, it makes a lot of sense. And um what you're saying makes tons of sense to me, too. You have to diversify. You have to be in different places, whether it's LinkedIn, X, a podcast, a book like we're doing here. Um it's in it's in a similar vein, you know, being out there in different places and creating sort of that funnel

and putting contact up there. >> It's hard work and good work, I suppose. Yeah, I think >> yeah, I think this is this is something you know why I thought that if I put top quality technical in-depth content for example on LinkedIn that will be what people know but over time I knew that I had to simplify my content a lot. I knew that I had to work long a lot on the first hook lines or whatever like I I realized how much I need to adjust the messaging >> and you know in in you know of course it depends on the platform depends what you do you know you need to adjust your communication style and your content style to what you're trying to achieve

We obviously now talked about like the digital and scaling that, but there's also the other side of building a tight but uh high quality network of relationships uh in your personal life either from work um conferences all those things. >> Yeah. How do you recommend people approach that side of the equation? >> Yeah, I would say networking and and building those relationships. That's something I personally regret that I haven't done more and earlier.

>> Mhm. And when we spoke at the very beginning about meta and about the things you know what what surprised me actually only at meta I realized that I have to do that right before meta I was I would go to work as a full-time employee I would put my heads on on heads on and that's it I'm disappearing the meetings would be waste of time for me colleagues would be you know people I work with just for for the sake of solving the the thing so I would seriously disregard any sort of networking and I I regret that meta allowed me to kind of uh learn this through you know baptis by fire but so high I would recommend going

back to your question I would say don't ignore this this is really important bit which will help you sooner or later h regardless if you're full-time employee or consultant >> having solid network of people who trusts you, who knows you are uh good at what you do and who likes you. That's going to be a game changer. And and how to do it? Be helpful, be nice, don't be a Uh don't be, you know, slacker, work hard, improve your communication, and do care for those relationships, right? Build them, spend time with people. Uh because yeah, I you really don't know. And and I'm not trying to be a transactional here. So I think like I don't saying you you have to you have to

vibe that that person is not nice and I had like my maybe two interaction with with them and they were always like it felt like the person thinks you know it's they are like way above you right and I I didn't like the guy but you know whatever you don't like everyone in the office right and then I get to meta guess who you know started bang my my my message box and I was like no way dude there's no way I'm going to move my single finger but that's not the end of the story because then I learned like my two other colleagues got to meta as well so two other people from the same company so three of us all together we

haven't spoke about that person >> then we got on the launch on that meta and and this this guy popped out and that was the first time we we together talked about that person and in independently. Three of us said like, "Oh, that was such an a-hole, right?" And then each of us is like, "You know that he actually approached me the day I moved my profile to Meta and I ignored him completely." >> So neither of us did anything with that person. We completely ignored them and the person never got to Meta. I know I, you know, I've seen his profile. Uh I know where where his career went. he never get where he wanted and was pretty desperate in his messaging to each of of

us. Right? >> So that's a counter example how not to do it. Don't be an a-hole. And uh on the other hand, you know, good example, I haven't get at meta. I I get to meta from recommendation. I've you know all my first my first geas consultant that was recommendation as well, >> right? So yeah those those relationships you build they they help you a lot and you may not feel this as an IC >> uh but when you are at the workplace where you're still employee just just do it because you have access now and that's really important and I think that's worth mentioning it >> Mhm. as a person who switched from full-time employment in an organization and run or you know you know employer

with a really good brand I can see how much more people care about you when you are and how much more access to people you have when you're an employee >> because when you switch to consultant you're you're suddenly not a peer anymore you're not a colleague when I reach to someone they don't think >> oh this is my peer Let's check. They're like, "Oh, what he want? He probably want a project." Well, he wants that. It's always you have you're a consultant on your forehead. It's never that easy anymore >> to to build this genuine relationship. It's always >> a little bit of >> this this this consulting saly uh thing. So, it's easier to build network and relationships and connections when you

are full-time employee. And if you have a good brand, good employee under you, then people also want to talk to you. It's so much easier. And uh so that would be my advice. Start doing this still as an FTE. When if you're a consultant, it's already too late for you. You're probably not working anyway. >> You're doomed. Don't try anymore. Just give up. >> You're doomed, right? But if you're still an FTE and you think about that, use your occasion now because it's just going to be much easier and much like it's going to be easier to build this trust and it's going to feel natural >> and you're not going to feel like a salesperson and that's going to be

different as contractor advisor or whatever. Yeah, I can I can definitely see a lot of parallels in my own life with this perspective too. It's there's a surprising little amount of people who care, will genuinely try to be useful to other people and helpful and trying to be good human in general just gets you further than we think. And especially now I think in this attention starved economy and with AI even more so the bar of being a good person in relationship is actually getting lower and lower. It's not we're not talking about rocket science. It's the simple small gestures.

My first client that I got >> Yeah. >> was when I was working academia still. I was helping a posttock >> learn programming maybe 15 minutes a day. >> Nothing much. >> Yeah. >> But then that was my connection to my first consulting gig. >> And I didn't think anything of it at the time, but it did sort of in unexpected ways lead to something valuable for myself years down the line. And I didn't aim for that. I didn't try to to get that out of it at all. >> Yeah. >> But it happened that way. And I guess like the more lines you have out, you have some years to let it like sort of marinate and compound and there's just a

bigger chance of the things that you've paid forward coming back to you in space. >> Oh, yeah. I mean, definitely. Come on. the I don't remember the statistics but but there is huge amount of job offers which are never even posted. The same goes to the you know contracting deals or or uh you know or potential or m deals. It's just you you don't see a lot of this because it goes between people who trust each other which build relationships which work together in the past company which help each other. Uh so yeah, just just do it. Uh definitely and and be genuine with that. Don't treat it as a transactional thing because if you're doing it as a transactional thing, it's going to be

tiring, right? Because it's not something that pays off immediately. It's nothing like you can, you know, okay, I will help that now. Now I they will do something like that. No, it's it's what you your example was amazing, right? Because you do something, you're just being a nice person. you were helpful and out of nowhere suddenly you know someone comes to you and and you realize oh great I helped that person one one of my deals was was actually that one as well so there was a person coming to me and I didn't even recognize that person I was like oh amazing I have I have a lead coming to me right and then I go to the the direct me the the

DMs and I like hey I actually helped that person in the past I didn't didn't recognize their face. It was not like I helped them and and I we build like whatever. I was like super busy at the time. I was at Meta. >> They asked me about bunch of things. I replied, they asked me on the meeting and I said like I don't have time to meet with you but I will try to help you here with my best. So you have a lot of back and forth >> and then I forget about that person that was one of my future clients and I didn't even meet with them but I did all I could at that moment to help them

do podcast when I speak with people I usually have someone reaching out to me and and we we can have a coffee or whatever and yeah I'm trying to build those relationships so hit me That's great. We will all link it down below for everyone uh watching here. And thank you again for joining today.