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031 - Building a Modern Data Platform
156 Plays1 month ago
In this episode of the Stacked Data Podcast, we're joined by Zach from Advancing Analytics to dive deep into the world of modern data platforms.
Zach walks us through his career in data and his current role at one of the UK's leading data consultancies. We explore what a modern data platform really is, why companies are investing in them, and what it takes to build one that’s scalable, reliable, and genuinely useful to the business.
From core stages and common pitfalls to ensuring business alignment and future-proofing, Zach shares the lessons he's learned delivering platforms for a wide range of clients. We also zoom out to talk about what it’s like working in data consulting—what skills matter, what a typical day looks like, and what makes someone successful at Advancing Analytics.
If you're interested in data architecture, consulting, or just want to understand what "modern" really means when it comes to data platforms—this one’s for you.
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Transcript
Introduction to The Stacked Podcast
00:00:02
Speaker
Hello and welcome to the Stacked Podcast, brought to you by Cognify, the recruitment partner for modern data teams, hosted by me, Harry Gollop.
00:00:13
Speaker
Stacked with incredible content from the most influential and successful data teams, interviewing industry experts who share their invaluable journeys, groundbreaking projects, and most importantly, their key learnings.
00:00:25
Speaker
So get ready to join us as we uncover the dynamic world of modern data.
Interview with Zach Stegers
00:00:34
Speaker
Hello everyone and welcome to another episode of the Stacked Data Podcast. Today I'm joined by Zach, the Head of Data Engineering at Advancing Analytics. Zach specializes in developing and building world-class data platforms and today he's going to share his insight and some of the most common challenges that he sees and also the solutions around them.
00:00:55
Speaker
Zach, it's great to have you on the show. How you doing today? Yeah, hi, hi Harry. That's a great introduction. Thank you. I'm doing well, thank you. Thanks for having me moving me on with you. No worries at all. um Well, that Zach, it'd be great, I suppose, to first off, for the audiences, as always, to get to understand yourself um a bit better, or your career journey, and more about your current role.
00:01:15
Speaker
Well, first off, my name's Zach Stegers. I've been working in some form or another with data platforms for about 15 years. I started off school age, not wanting to have anything to do with IT because i used to enjoy it. I liked tinkering around with my computer and whatnot.
00:01:34
Speaker
And I thought working in that industry might ruin it for me. But actually it's the complete opposite. I ended up falling into it. I don't think anyone necessarily grows up saying I want to be a data engineer or whatever.
00:01:46
Speaker
I kind of found SQL at one point in my career, working for a small web agency. And there I was development support was the role that I was in.
00:01:59
Speaker
And that included making some really basic HTML and CSS changes to some websites and then advancing on to some basic ASP.net SQL servers behind that.
00:02:12
Speaker
And I found that the the SQL servers is really where my passion was. i quite enjoyed looking after those, writing queries and and learning SQL. And I just got deeper and deeper into that area, eventually picked up SQL Server reporting services and started doing a little bit more in in that side.
00:02:30
Speaker
SSIS came a little bit later and starting to actually understand data warehousing, building out platforms using SSIS and then serving out to Power BI when Power BI came along instead of SSRS.
00:02:44
Speaker
And then started moving into Azure as that came to be. That's evolved a lot from the early days of Azure. And now that is basically all we do. Everything that we do is based in the cloud.
00:03:00
Speaker
Everything that we do is based on PaaS, Platform as a Service components, really. And yeah, that's my very vague overview of my career and in in a nutshell.
00:03:11
Speaker
Well, I think many professionals, they fall into data and don't grow up dreaming of it. But once they fall into it, they definitely fall in love with it. And ah it's great to hear, obviously, that progression. that One thing I'd love to to hear from you, Zach, is so the progression in technology, you know, from SQL Server and and these on-premise
Evolution of Data Platforms
00:03:30
Speaker
solutions.
00:03:30
Speaker
How was the progression rate back then compared to what the progression rate's been, say, over the last... five to eight years when the clouds really started to take form. It'd be great to get your observations in that space.
00:03:42
Speaker
That's a good question. It's been, it's been fresh pit in comparison because it used to be that, you know, back in the SQL Server 2005 days, you would ah literally receive SQL Server on a disk and have to install it on a server.
00:03:56
Speaker
And then i don't think another version came out until 2008. So, you know, that was like three years between there being any development significant bit. any kind of significant innovation.
00:04:09
Speaker
Whereas now, tools like Databricks and others, we see that there's a monthly release cadence. And these aren't small changes. These are really quite significant improvements to their platform.
00:04:21
Speaker
So the rate of change has accelerated enormously over my career. It's really difficult to keep up. Because there's multiple tools out there. Just in Azure, you've got Fabric and you've got Databricks now, and both of them are evolving. They work well together, but could build with just one of them and and kind of navigating that journey and trying to decide what the best tooling is for you is it's really quite challenging things and that's i suppose where organizations like ours thrive in trying to help you understand which tooling is going to be best for you and and why that isn't a simple decision making process really it comes down to the
00:05:02
Speaker
The skill set of the team comes down to the cost that you have, also the the budget that you have for your environment, how much do you want to spend, any security requirements, networking requirements, all of this kind of ancillary stuff around the data platform needs to be considered before you actually jump into building one, really.
00:05:22
Speaker
Yeah, I mean, now the the amount of options and solutions out there can be
Advancing Analytics' Approach
00:05:27
Speaker
overwhelming. And that's before you get into the build versus buy argument for tooling as well. So you obviously mentioned that's where your expertise comes in, Zach. So you're the head of data engineering at Advancing Analytics.
00:05:39
Speaker
yeah Could you give us brief overview of of who Advancing Analytics are? Advancing Analytics are, we're a UK-based consultancy, but we work with Virtually globally, there's there's been one one project in Australia that we just couldn't make work based on time zones.
00:05:55
Speaker
We are distributed team, we say now, so we are we're room remote, essentially. we We have a couple of small offices where people can go and work if they want to, but it's not a necessity.
00:06:08
Speaker
So, yeah, we kind of work with our customers using a lot of collaboration tools like Teams and Zoom and what have you to hold workshops. We do visit people on site as well and run workshops.
00:06:21
Speaker
Just to uncover their requirements more than anything, i think a lot of people think, yeah, we want a data plan. We want to be able to measure A, B, and C. But then, again, as I mentioned, there's a lot of ancillary stuff behind that that you need to think about and consider before you actually start building it.
00:06:37
Speaker
And we talk a lot now these days about product owners for analytics, for analytical data products. And That would be my colleague East, who's head of analytics, really um defining and shaping a lot of that.
00:06:51
Speaker
And then we have my colleague Gabby as well, and she's fantastic in the data science space. They do a ah lot of wonderful things with people's data, helping them implement LLMs these days and that kind of thing.
00:07:03
Speaker
My boss, Simon Whiteley, he's the kind of tech guru across all of it, really. And he, along with a couple of our other colleagues, runs ah a nice YouTube channel covering a lot around Spark and Fabric and other kind of features across data platforms.
00:07:21
Speaker
So yeah, check that out, Advancing Analytics YouTube, little plug. and Amazing. I'm sure there's some nuggets of gold on there. So yeah, definitely go and give YouTube a channel um ah a listen and a watch. we'll We'll put the link in the comments. um So today, zach we're going to be talking about modern data platforms. You've obviously had this journey from on-prem to now all the the plethora of tools are available. What is a modern data platform and why would a company look to implement one? Zachary Hicks
00:07:53
Speaker
Good question again. So a lot of what we talk about when we deliver training to customers, and we try to kind of set the scene of why someone would want to implement a modern data platform, as it were.
00:08:07
Speaker
And we I know people can't see me doing finger quotes on on a podcast, by the way, but you can. say So, yeah, we talk about things like self-managing. We used to have to have a data center that we installed servers into. We installed a cooling rack here, all of this stuff.
00:08:24
Speaker
There might be physical security that we have to look after. All of that kind of goes away with moving to the cloud. We just have to look after the actual resources and the components and how data moves from A to B and all of that good stuff.
00:08:38
Speaker
So we become more self-managing in our infrastructure. There's also just so much more agility. You can scale your compute up and down so much easier. Again, thinking back to kind of SQL Server days and having maybe an always on cluster, you would have to potentially buy and an entirely new server, install SQL server on that, set that up, add it to your cluster.
00:09:01
Speaker
And that could take weeks if you wanted to scale that out in that way to add like a read-only node to your data source. And if you do it, if you if you're trying to scale out a data warehouse, it's even harder.
00:09:13
Speaker
And have downtime involved in that, you have basically have to spawn up a whole project to be able to manage just scaling your servers. And that isn't the case again in the cloud. You can just, with a couple of clicks of a button, scale compute, or you can even let it decide to scale for you.
00:09:32
Speaker
So it's a lot less rigid in that in that sense. That doesn't come cheaply either. you know yeah It becomes quite difficult to do analysis of how much this is actually going to cost.
00:09:43
Speaker
So working those types of things out is, again, another thing that we try to support our customers with. And then, of course, you just have more flexibility in the types of analytics that you can do.
00:09:55
Speaker
a you can The types of data that you can ingest and in a process, ah you can do things in Spark that you could just simply not do in the old SQL Server days of processing images and using image classification models to strip metadata out of those. And then all of a sudden you can build a model based on images.
00:10:17
Speaker
That is just vastly more difficult or time consuming in the old world. So it's really just about flexibility more than anything. It's about being able to respond appropriately to business demand, business need.
00:10:32
Speaker
But also, I think having the flexibility in your platform from an engineering perspective, to be able to ingest a plethora of different sources and have truly kind engineered your pipelines to be flexible, to be able to handle those with as small a code footprint as possible so that it's nice and maintainable, easy to look after, easy to change, all of that good stuff. That's kind of what a modern data platform looks like to me.
00:11:01
Speaker
Great to to hear. is It's amazing how, I think you said, the agility that that it gives organizations and the abilities which were no longer possible and the amount of value that you can look to derive from that in advance, not just reporting, but then predictive analytics and then building automated products, consumer-based products, data products, which people can use. It's all powered from a platform where you have your governance, you have your security and analytics.
00:11:27
Speaker
hopefully i can build data quality and trust within that data as well. But I'm sure that's probably one of the challenges that we can can get onto.
Building Data Platforms
00:11:35
Speaker
Zach, it'd be great to hear a bit more about how ah advancing builds data platforms and what you know what are some of the key steps stages or milestones when building a data platform that the people need to consider and and which ones are, I suppose, the most important in that that process to to really get right.
00:11:57
Speaker
Because I know if you get some of them wrong, there can be long-term consequences, which can be costly. So yeah, it'd be hit great to sort of educate the audience on some key areas that you really need to nail. Sure, yeah.
00:12:08
Speaker
So in terms of tooling for the data platforms that we build, we are mostly operating in Azure, although we do some work elsewhere as well in AWS, for example.
00:12:20
Speaker
but we We focus mostly on Databricks, um but that you know we is evolving all the time. Databricks is growing enormously. you know Unity Catalog has changed the game with Databricks a little bit.
00:12:32
Speaker
And Fabric coming out is, is again, quite quite the competitor potentially to Databricks. So, so You know, we we work across the two stacks, really.
00:12:45
Speaker
we have We have people who know an awful lot about both of those. And was having this conversation with someone yes just yesterday, actually, that a normal kind of data team within um an organization probably needs to pick a technology that is right for them and then specialize and and kind of deepen in that technology.
00:13:07
Speaker
Whereas we, where we work with customers who could be in any one of the clouds or could want to implement Fabric, could want to implement Databricks, we need to have a really kind of deep understanding in both of those and how you engineer a data platform in either of those.
00:13:25
Speaker
So, yeah, were we're relatively flexible in terms of the tooling, but we do focus in on Azure and on Databricks at the moment with a few little AWS bits. But In terms of the milestones and the stages of building a data platform, there's almost always a discovery phase, and that is incredibly valuable. went on Whether someone ends up going with us to support them through that build phase or not, the discovery always results in
00:13:58
Speaker
a document that will detail exactly what their platform is going to look like, how things are going to integrate the network top topology, everything that you could kind of think of when needing to build a data platform. So the discovery is an incredibly valuable ah piece.
00:14:15
Speaker
Now, again, customers can take that away and build that themselves after like they've had the discovery with us, or they could work with another car there if they so wish, all they can can continue working with us. And obviously, we hope that that's the choice they make, but it is it's down to them.
00:14:32
Speaker
After that, it's all about getting things configured in the data platform in terms of infrastructure. So deploying infrastructure, having that all networked and all of the components able to talk to one another, that is when you can really start to actually deliver some value to the organization. Because until that stuff stood up, you can't really do any data ingestion or anything like that.
00:14:57
Speaker
And then we build things in terms of, we we always use data lakes. We process the data through different layers of the lake. People are familiar with the Medallion architecture, for example.
00:15:08
Speaker
And It's always, and there's always a bit of a mini celebration when you get data ingested and then another one when you get it through to the next layer and another one when you get it through to the next layer.
00:15:19
Speaker
But then the real kind of joy, I suppose, comes when you're working with a customer and they finally see, you know, after probably a couple of weeks of configuration, set up, ingestion, we're finally able to put something in front of them in terms of like a a Power BI dashboard to the would probably cover a very small amount within ah a space of a couple of weeks, but it's kind of showing value. It's starting to build trust and show the direction of travel to that customer so that they can start to come along on the journey with us a little bit more.
00:15:53
Speaker
Some customers, you know, they have a deeply technical team and they are there from A all the way through to to B. Others are less so and they kind of put their trust in us to deliver that data platform. And it's only when they see that first dashboard or that first output that they really start to see any kind of value.
00:16:13
Speaker
So yeah, it depends on the customer. But yeah, those are the core milestones that we work towards really. Yeah, I think that discovery phase is so, so important. It's like you're extending a house and you don't know what you're going to get until you start peeling back the curtain and there can be challenges in there that are unforeseen. And I think as part of our discovery is not just what's there, but it's where do we want to go? What you want to get out of this platform? What business value are you really looking to
Aligning Data with Business Value
00:16:40
Speaker
drive? Because I think then...
00:16:41
Speaker
That type of understanding will depend on what you need to build. And and it's like with any thinking data, working with the business to understand what they want from data is, I think, at the core. Because um if you're you can build the the most advanced data platform, all set up for machine learning and advanced and analytics, but if they're looking for just some financial reporting, it's maybe a bit overkill. You hit the nail on the head, business value.
00:17:09
Speaker
It all comes down to business value. and you can have the the the most well-engineered platform, but if it's not providing any value, it really shouldn't be there.
00:17:21
Speaker
That's kind of the crux of it. And equally understanding what good looks like and when enough is enough. Whenever you're speaking to testers, there's often a question of how do you know how much testing is too much testing? And you can test stuff from so many different angles, and it's a bit of a theoretical question almost. Yeah, I think it's also that speed versus yeah versus the sort minimum viable product, the MVP.
00:17:47
Speaker
ah You want to get something out to stakeholders, to the business that is going to be valuable to them as quickly as possible. There's no point refining. Businesses are fast moving. So I think that comes at any stage in the data lifecycle, whether it's a dashboard or or a platform. What what can be there the first stage you can get a ah um a minimum viable product to them?
00:18:07
Speaker
Yeah. And also, I think when people see that MVP, that's when the ideas really start kind of flowing and popping and the wider business starts to get wind of it. And they're like, oh, could you adjust our data source and do some stuff with it? But the whole data and analytics thing we see just snowball in organizations from delivering that first MVP is often there.
00:18:30
Speaker
a huge catalyst at that point to more and more requirements coming through. So, you know, the the journey doesn't just stop. You're always kind of developing this and then you develop ah a set of dashboards that gets you to a certain point and you're able to measure all of these wonderful things. And then people have more ideas around, well, actually, if we bottled it like this and if we changed it up to measure this, then we'd understand this. And it just keeps on evolving for forever, really.
00:18:58
Speaker
Yeah, it's cold constant iterations. That moves us nicely onto a question which keen to understand, I think you could hopefully pass on some insights and strategies to to the listeners on how you guys ensure that the the end products, and in this case, ah a data platform, actually fulfills the requirements for a business, a client, or a stakeholder. What strategies do you employ to ensure that so you know when you you do deliver this product,
00:19:23
Speaker
that is actually driving business value, because I think that's something that anyone listening from a data scientist to an engineer could could take value from understanding. Yeah, so we, i asked it I think you nailed it earlier again, like talking about business value, but uncovering what that business value is, is the difficult part and and making sure that people understand what it is that they are asking for, but also that the the other side, the technical side, understand what is being asked for.
00:19:54
Speaker
We kind of run these workshops to yeah all the stakeholders in a room and we we do some whiteboarding and whatnot and try to uncover and prioritize people's requirements.
00:20:06
Speaker
Data people, I think in particular, are very, very literal. There was a joke that was a good long while ago about someone saying, can you buy a bottle of milk?
00:20:17
Speaker
And if they have eggs, can you get six? And they went home with six bottles of milk because they did have eggs. So there was like, you know, it's making sure that you really kind of tease out those requirements so that mistakes and silly things like that don't happen.
00:20:35
Speaker
That is really difficult and it' it requires keeping the business engaged, right? So we never want to work in isolation of of the organizations that we support. We want to take them on the journey.
00:20:48
Speaker
All of our projects are delivered in an agile form and that includes all of the normal ceremonies of an agile project. So we are continuously playing back.
00:20:59
Speaker
What we've changed this previous sprint, we're showing the business to say, hey, here's here's what happened last spring. Here's the challenges that we had last spring. Here's the things that that went really well. And here's the output.
00:21:10
Speaker
And that gives them an opportunity to pass comment on it and say, actually, I was hoping it would kind of be like this, or maybe they're blown away by it and they're super pleased.
00:21:21
Speaker
but Yeah, taking them on that journey, making sure that they don't disengage is really important. From ah more technical perspective, because we often are either building this platform and then we hand it over to technical teams within the organization, or perhaps they don't have a technical team yet.
00:21:42
Speaker
We sometimes build what we like to build collaboratively. If our customer has a technical team, we want them to embed someone from that team into our team whilst we're doing the build phase.
00:21:56
Speaker
Not only does that make it cheaper for them because they are providing a resource, but it also means that that person has gone the entire journey. And we often find that actually, yes, they get the platform, which is what the aim was, but also they gained a lot in terms of ways of working.
00:22:16
Speaker
They aren't necessarily familiar with Agile and they start to adopt that and they run with it. They become a lot more fluid with changing requirements and and things like that.
00:22:26
Speaker
and just helping organizations adapt and grow to be more serving of the business, seeing the business as their customer. You know, technical teams sometimes are, there's a barrier between the tech and the business team.
00:22:42
Speaker
Yeah, that that doesn't work well when you're trying to collaboratively build something. So knocking down those silos, bringing everyone together, making everyone talk to one another about the requirements and delivering something fantastic is what we're all about, really.
00:22:56
Speaker
Amazing. I think that's, yeah, a great, great lesson is that that collaboration, the communication, and yeah, not taking something that someone's asked at face value, you really probing, I think that the five whys strategy, you know, why do you want that?
00:23:12
Speaker
And why? what What about that? And why to that? You take the stakeholder on that journey because you uncover more and more. And then, yeah, you should be left with the core the core issue called business impact at once.
00:23:25
Speaker
I think also the comment that you made on embedding and how you look to embed one of the the current team is great because, ah They get to build up all of that context and all of that knowledge, which otherwise can be quite foreign if you just deliver a product at the very end. And that's something you could take into any part of data. When you have these champions that that are enjoying data and what's possible, it's taking them very closely on that journey so they feel part of it. I think that gets much better engagement as well.
00:23:55
Speaker
we we We also use a technique called BEAM. So you mentioned the five Ys and there's the, in BEAM, there's the seven Ws. It's like who, what, how much is a bit of a cop-out one? There is a W in there.
00:24:07
Speaker
There's a few different questions. There's a chap called Lawrence Kaur, who wrote a book about the Agile Data Warehouse, which is a really good read for anyone out there getting into data warehousing or looking to upskill on how you run these workshops.
00:24:21
Speaker
And was all born of kind of journalism. What are the questions you have to ask to... make a good story and data storytelling is is a thing these days and being able to ask questions of your data.
00:24:36
Speaker
We use that as a method of gathering requirements as well on the analytical side, although we've evolved it somewhat because there's another method called modeling and my colleague Johnny Winter is kind of spearheaded, mashing the two together into something to get people's requirements going.
00:24:53
Speaker
Amazing. That's ah great, great to hear. So it's been great to hear your approach and and views on on modern platforms, why the beneficial, the key milestones and yeah how to make sure what you're building links up. But I also want to touch on sort of the the skills and experiences that you feel are important for Someone looking to build a career as a data consultant.
00:25:18
Speaker
Data consultancies offer huge amounts of opportunities for people. They get to work on multiple different environments. With that comes many different challenges. And these challenges in different environments are often ways to extremely accelerate your growth trajectory and understanding of data. So they're obviously great roles to get into to really accelerate your learning. But what are some of the key skills that people would need to have a job as ah as a
Consulting Principles and Daily Life
00:25:43
Speaker
consultant?
00:25:43
Speaker
I think it is less around tooling. So less focus on tooling and more focus on the principles and the why of what we're doing.
00:25:56
Speaker
And I think we see that as as people enter data engineering as maybe relatively junior and so it's more about the tooling. And as you become more and more senior, ah you swap it into Yes, you understand the tooling, but you also understand the reasons why a certain tool may be good or bad because of the principles that are behind it.
00:26:19
Speaker
And, you know, we're we're we're often quick to jump to to a new technology and think, okay, it's the latest, greatest thing. But if you come at it with...
00:26:31
Speaker
a background of implementing these data platforms and you understand the principles behind all of the different tooling and processes that you you implement, then you're much better equipped to be able to assess that new tool as to whether it is actually appropriate in your environment.
00:26:49
Speaker
Does it have the functionality that you need to implement certain certain principles? you know like In an engineering world, how does something like an orchestration tool manage dependencies? How does it manage queuing of processing entities? How does it manage grouping of entities?
00:27:08
Speaker
So I think whilst the tooling is important because that is ultimately what you use to implement the principles, if you don't understand the principles, then it's difficult for you to pick up a task and deliver that information without needing a lot of support to understand the why. Because if the task is said, go and implement this, you'll just go and implement that.
00:27:32
Speaker
And then you maybe haven't advanced anywhere. You've just kind of clicked some buttons and done some stuff. Whereas if you understand why you're implementing that and what value it is that you're trying to get out of it, I think you understand a lot more value-driven approach.
00:27:46
Speaker
So... Yeah, I think that would be my advice to most people is to make sure you understand the principles behind the tooling, not just the tooling. I couldn't agree more. I think that's that's great advice. there's there's With the the birth of the modern data stack and and its continued growth, there's so much focus in on tooling, yet the the the people that I see that excel in in careers are the ones that aren't necessarily experts within a specific tooling, but as you say, they understand the principles behind all the tooling. It gives people a holistic view.
00:28:20
Speaker
and They understand the mechanisms behind it and are able then to make um big picture decisions on knowing what the pros, cons, and limitations are. And I think that's something that but not enough people focus in on. substance So that's great. I mean, what would be some of the core principles that you would encourage people to to explore or to to understand?
00:28:45
Speaker
i think a data modeling is certainly one of them. Almost everything that we build is in from an amateur. analytical model perspective is based on Ralph Kimball's teachings. So of Kimball data warehousing, definitely all of the principles behind that would be a really great place to start.
00:29:04
Speaker
Orchestration as well is another big one. Understanding the difference between kind of SQL server type engines versus NPP engines like Spark, where they fit in.
00:29:16
Speaker
The separation of storage can keep compute, why that's good. Infrastructure is code, CICD. There's so much to learn, really. Yeah, oh that's a great starting point. yeah There's a few there for people to dive into, for sure. so Coming to the last few questions, Zach, I suppose, look, really keen to understand what the the day of a consultant looks like at advancing.
00:29:41
Speaker
Okay. So it does depend somewhat on your level. So we have kind of junior... consultant, senior, and principal levels. And it it does depend where you are in that scale. And we do our best to assess when people are being interviewed as to where you might sort into our team there. But day-to-day for, say, a consultant or a senior, you're likely going to be on a project team.
00:30:04
Speaker
And that could be either supporting a discovery. It could be running workshops as as a discovery, or it could be doing actual delivery work for a customer, so implementing something.
00:30:18
Speaker
We have a few different offerings. So we have like architecture reviews, which are 10-day engagements where we'll come along and kind of probe everything about your ah your architecture to try and help you improve that architecture and in some way.
00:30:34
Speaker
So you could be being involved in one of those. We do cost and performance analysis, and you could be involved in one of those. Unity catalog migrations. Yeah, there's there's kind of a plethora of different things that you could be involved with.
00:30:48
Speaker
Never for a dull day. Never a dull day in consulting. But equally, we have accelerators. So we have our platform Hydrate, which is... based on data rigs, it uses all first-class resources within Azure.
00:31:02
Speaker
And you could be developing against that and and helping future customers when we eventually roll that out to them. So yeah, there's lots of different things to get involved with. And engagements could be anything from ah a single day talking to a customer all the way up to a year-long engagement working with a customer to actually deliver a fully-fledged data platform with all the bells and whistles.
00:31:27
Speaker
That's great to hear. As I said, never a dull day. It sounds like someone's going to get the opportunity to work across a huge range of projects and upskill in multiple different verticals, which is always great. So what does it take to join the Advancing Analytics team? What does it take to be successful at Advancing Analytics?
00:31:48
Speaker
Oh, so... It's really stereotypical kind of like go-getter attitude. But yeah, people people who are you know invested in their own careers and they are interested and want to drive things forward um always perform well.
00:32:03
Speaker
I think you know most everyone... advancing is is fully engaged and wanting to contribute and deliver to the absolute gold standards of quality.
00:32:15
Speaker
And that that is what we're about. We like to be pioneering in technology, what we like to drive the industry forward in how things operate. We work with product teams quite a lot across Microsoft and Databricks to help them understand the the challenges that we're seeing with their technologies.
00:32:32
Speaker
And yeah, kind of having that appetite and thirst for for learning and growing is one of the most crucial things. You know, if someone was to interview with us and they didn't necessarily nail all the questions, but they show a real kind of keen appetite for growth and they want to understand maybe why they didn't get some of those questions right.
00:32:51
Speaker
I think that is the best attitude to adopt.
Company Culture at Advancing Analytics
00:32:54
Speaker
So people with a growth mindset, I imagine your culture is is centered around learning development and and constant improvements, would be fair to say.
00:33:03
Speaker
Pretty much, yeah. i mean We have a pool of talent development days that we give to to everyone every year, and they can take those off, book themselves away from project to say, you know, I want i want to go and look at this technology, you'll want to scale up in this area.
00:33:18
Speaker
and But to to me, just throwing yourself into things on projects has always been my way. It's how I have learned in my career. I think you have those real world interactions and requirements to work from when you do that.
00:33:35
Speaker
And that's a always been my method for upskilling, but not always everyone's preference to baptismify yourself. So yeah, we different approach for different people.
00:33:47
Speaker
Brilliant. Brilliant. Well, look, Zach, and that's all we've got time for today. But it's been a pleasure to to have you on and yeah hear about the life ah ah advancing and and some of the great works and and insights that you've been able to share on ah modern data platforms.
00:34:02
Speaker
Before you go, I've just got the last few quickfire questions which we ask all of our guests. So the first question... How do you assess a job opportunity and how do you know it's the right one for you?
00:34:13
Speaker
I've never really done that, to be honest. I haven't haven't necessarily planned my career a great deal. i've I guess maybe following on from the last question, I've always just kind of thrown myself into things. and Go from there.
00:34:27
Speaker
Yeah, that's always kind of led me yeah on an upward trajectory. So, yeah. Just get get get involved at whatever peaks you are your interest. and And what's your best advice for people in an interview?
00:34:39
Speaker
and be Be engaging, ask your own questions, come prepared with your own questions and have done done a little bit of research. um I think you can you can almost always tell when someone hasn't done any research into your organisation and doesn't have any questions. or and Or they're just the generic questions, right? I think that's the other one. I think you need to ask thought-provoking questions, which are are unique and and you know makes the hopefully makes the interviewer go, ah and and have to have to think. I think that shows you know your your critical thinking ability.
00:35:12
Speaker
you know The questions like, what's your culture like, tend to, to I think, not have as much of a of an impact what I've heard. And yeah finally, Zach... If you could recommend one resource to the audience to help them upskill, what would it be?
00:35:30
Speaker
YouTube, probably. a free A free one. There is so much free information on the on YouTube and other elsewhere on the internet. And yeah, making use of that.
00:35:42
Speaker
Sometimes bringing it all together and structuring it so that you you know you have the kind of full picture is really difficult. But yeah, if you're if you have a particular interest in an area, just searching that on YouTube. I watch a lot of YouTube and I find it incredibly valuable.
00:35:58
Speaker
Yeah, it's for a visual learner as well. You get to visualize and see and see stuff happening in in real time. I know there's some great content creators out there. So yeah, do do do dive into into that. Well, Zach, it's been a pleasure. Thanks ever so much for for joining us. Thank you.
00:36:15
Speaker
Yeah, the worst no worries. No worries. Thank you. Thanks, everyone. And we'll see you next week.
Conclusion and Call to Action
00:36:22
Speaker
Well, that's it for this week. Thank you so, so much for tuning in. I really hope you've learned something. ah know I have. The Stacked podcast aims to share real journeys and lessons that empower you and the entire community. Together, we aim to unlock new perspectives and overcome challenges in the ever-evolving landscape of modern data.
00:36:43
Speaker
Today's episode was brought to you by Cognify, the recruitment partner for modern data teams. If you've enjoyed today's episode, hit that follow button to stay updated with our latest releases.
00:36:54
Speaker
More importantly, if you believe this episode could benefit someone you know, please share it with them. We're always on the lookout for new guests who have inspiring stories and valuable lessons to share with our community.
00:37:05
Speaker
If you or someone you know fits that bill, please don't hesitate to reach out. I've been Harry Gollop from Cognify, your host and guide on this data-driven journey. Until next time, over and out.