📊 Recruiting Corner: Business Intelligence
Learn about current job openings and what it's like to work on Cloudflare's Business Intelligence team in Austin, TX.
All right, well hello from sunny Austin, Texas. I am so happy to be participating in the BI Takeover on Cloudflare TV and combining it with our bi-monthly show, The Recruiting Corner, to talk about all things business intelligence, data science and data engineering.
And so today I'm excited to introduce some of my colleagues that I've been fortunate enough to work with for many years as hiring leaders at Cloudflare and so we're going to talk about the openings that they do currently have in Austin.
So I'm going to go ahead and kind of go around Robin so everyone can introduce themselves starting with Nikole.
Hi my name is Nikole Phillips and I am the data analytics manager here on the business intelligence team in our Austin office.
Awesome, Katrina. I am Katrina Riehl, I'm director of data science in the business intelligence team also in Austin, Texas.
And Chandra. I'm Chandra Raju, I'm the director for data engineering.
I'm also part of the business intelligence team based out of Austin.
Awesome, awesome. Well I think a great place to start off is you know how each of you ended up at Cloudflare.
So Katrina, tell us since you were the first one to join out of this group.
All right, absolutely.
I actually heard about Cloudflare several years ago to tell you the truth.
I am part of a non-profit organization outside of Cloudflare where we actually Cloudflare on all of our websites and so as one of the more glamorous parts of my involvement there I'm one of the web administrators so I've been receiving Cloudflare alerts for a really really long time.
And so when the opportunity arose to come here I was very excited to join Cloudflare and get some you know data science you know in the door.
So really excited to be here. Awesome, how long have you been here Katrina?
Almost two years, two weeks from now in fact it'll be two years.
Coming up on that anniversary, awesome. How about you Nicole?
Okay yeah for myself it's interesting because I ended up learning about Cloudflare by a glass door alert and I seen a opportunity here so when I looked into what they're doing in the community it made me even more intrigued so I definitely decided to apply and cybersecurity was one of the domains that I had not worked in throughout my career so that intrigued me even more because I like to be challenged and then being able to be on a new team and work with the colleagues that I work with it was a win -win across the board to be able to use data to continue to let Cloudflare be a dominant force within the tech industry.
Awesome, awesome and how long have you been here?
It'll be two years actually in May.
Okay, okay so I started a couple weeks after Katrina. How about you Chandra?
Yeah for me journey was also interesting like I've been with travel industry and telecom industry before.
Secure domain seems very interesting with all companies now moving to cloud and security being one of the focus for a lot of companies and the amount of challenge they have with the new products and like some of scale of Internet they handle.
I want to see something challenging to see build something new and interesting scalable and how can we collaborate with partners.
The Cloudflare opportunity seemed to be very interesting and like the way they have been in everywhere focusing on non-profit helping them to empower a lot of different organizations this seems to be natural for me to explore this opportunity.
I'm glad I'm here with a wonderful team of members here. Yeah, no absolutely and I'll go ahead and let you all know that we all worked together when I saw all you guys leave and go to Cloudflare.
Who is this company? It was a reunion right when we all met you.
That I've been working with and as I started to become more familiar with Cloudflare I really really could identify with the whole concept of building a better Internet.
The mission really was something that I had a passion for and you know security and working in the cloud are all things that are exciting and a transformation that just is happening globally and so when the opportunity to be the first recruiter in Austin came about I jumped at the opportunity to work with you all again.
So yeah great to put the band back together.
Let's talk a little bit about what you all do at Cloudflare. So at a high level Chandra can you start off by telling us what the BI organization does?
Yeah, thanks Todd.
BI's role is to unlock analytics capabilities and generate critical insights for our business teams.
How we work together as a team cross collaborate with different business partners within Cloudflare and allow them to be taking more data-driven decisions.
We collect data from first party and third party data sites like how we convert the data to an information and insights which can help our business to grow.
How we collaborate with them giving them like to democratize data, give them tools to access our data in an easy way.
Our goal is to empower business to take that next level of data-driven decisions which we have been doing like successfully within a short span of time BI team has been formed.
As we said like we're just getting started. We have a lot to do but we're making the right decision, right investments and collaborating on right use cases with our stakeholders.
I'm glad like we are reached the where we can expand further and contribute further to the team success.
Awesome and so let's take a click down and talk specifically about the data engineering team Chandra and how that fits within the team.
Yeah, I think BI has said like we have data engineering, data analysts and data scientists.
We all work as a team. Data engineering specifically we focus on like how we bring in different data sets from like first party or third party data sets.
How the data can be brought in a way it's scalable, cleansed and transformed in a way that our partners can consume it whether it's a data science team or a data analyst team or business partners.
So the goal is to build that scalable pipeline which is reproducible, repeated in automated fashion and then work with our stakeholders to see how we can build also more like self-server tools to access the data in a more seamless way.
So our goal is to enable the data within the organization and then help others to use the data in a more effective way to drive further insights.
Awesome and then from a data analytics perspective Nicole tell me a little bit more about your team.
Yeah so once the data engineering team goes through their process my team what we do is we use that data from the perspective of engaging with the business to really be able to understand what business problems are you trying to solve, what business needs do you have and then taking that information back and synthesizing it into a format where it can be consumed by our business users so they're enabled to be able to make data informed decisions as it pertains to their business goals.
Now it sounds simpler than what it really is because we have to go through a lot of EDA which is exploratory data analysis to really be able to understand if the data is going to meet the need of the business problem or the use case and that's where the collaboration with the big data engineers comes in and even and also our stakeholders because we have to all come to a consensus about the integrity of the data before the analysis even begins for us to be able to provide that insight to them.
Excellent excellent and then Katrina on the data science side how does that tie into this?
Sure if you want to look at this as kind of a flow of information through the team like data engineering brings all the data in they make it usable they collect a lot of data and history things like that then you know analytics comes in they bring all the insights and make sure that people have the decision making data that they need and then on the data science side we really focus on automating decision making so the idea that we can take all of that data and mine it and be able to create artificial intelligence-based systems that would allow us to be able to automate some of these decision making processes so we really focus primarily on machine learning within my team.
Oh okay and so you know a lot of times in data scientists or data science I hear about people being data scientists or machine learning engineers.
Can you tell me a little bit about the difference and how significant?
Sure we actually we have both on the team by the way right so we did split this up into two different roles so we do have data scientists and their primary responsibility is going to be modeling so getting very close to the data mining the data for really important patterns understanding the data very very in depth and being able to tune and create the machine learning models then on the machine learning engineer side that's really the engineering piece that's going to unlock that right so once we have a model we want to be able to deploy it we want to make it usable we want to integrate it with systems downstream and we want to make sure that it's also integrated within our platform and so there's quite a bit of engineering in order to be able to make a successful project on both the data engineering side as well as on the machine learning engineer side in order to make that entire flow work.
Okay awesome and I know that there are so many different tools and technologies that are common in the marketplace.
Katrina can you tell us a little bit about the tools and technologies that we do utilize at Cloudflare and maybe just from the BI layer all the way to the back end?
Sure so I actually there's a laundry list of technologies by the way that we use because you know obviously this is a pretty big domain and you've talked you know Chandra and Nicole and myself right we actually have two major platforms that are under development within our our organization so we have a data engineering platform which can also be broken up into sort of our data ingestion framework as well as our data access and data access layer.
Then we also have a machine learning engineer or I'm sorry machine learning platform that allows us to you know model to be able to deploy machine learning models and these two actually feed on each other if you think of it as like the machine learning platform as being both a consumer as well as a producer of the data that feeds back into our data engineering platform.
So within this whole stack we actually have like I said just a ton of technologies that are being used and I know I'm going to skip some but I'm going to try and hit the highlights as much as I possibly can but obviously we can't do anything without Google Cloud right that's what kind of the basis upon which we're using everything so we can you know we do have you know GCS as well as BigQuery on top of that right so obviously SQL as well as Spark is going to be used in that the primary languages I would say that the team is using are going to be Python as well as Scala with that and so and SQL if you want to you know consider that a language but that's a different story but anyway like I said we're going to be using Spark as well as there are some other tools around this that I also want to mention so have to talk about Kafka as being something that we're using we're using Docker we're using Kubernetes we're using Airflow over on the data science side we're also using Argo and we're also we have RESTful interfaces and gRPC for remote calls back into the systems that are being created there and I also should mention on the machine learning side we also are using Kubeflow which is something that allows us to be able to ease some of this and then moving over to the data analytics side for sure obviously using quite a bit of BigQuery also using Python also using Tableau, Kibana dashboards, visualization through our own customized apps as well so we do have in-house visualizations and dashboards that we're creating as well and those are going to be you know web applications that you know react front ends and then also having you know the back end using our existing infrastructure and with that one I also want to mention that we're also using some Cloudflare products within our apps that are being deployed right so just a shout out to Cloudflare access to Argo some of these things are definitely so we're you know using our own products within the applications that we're deploying and kind of the last piece I do want to talk about a little bit since machine learning is kind of the domain that we're in we're very much tied to the PyData ecosystem and so that's something that we spend quite a bit of time working in and that requires you know the scientific computing stack which is going to include NumPy, Pandas, Scikit-learn you know we use a lot of light GBM and XGBoost all of these other pieces that kind of go under the hood and we also have a tendency to move more towards PySpark as opposed to Spark and Scala that might possibly be used upstream from us and then one more language I did want to mention since we are I'm trying to be as complete as possible got to give a shout out to I know Go is also being used on the back end for some of the application the web application work.
Yeah I'd like to come and add to Katrina said like it's a long list she said if you put in the focus majority of them 90-90% is open source technologies that we adopt we try to pick a technology that is like platform vendor agnostic so if I want to move from a cloud platform to an on-prem platform or hybrid platform how can we move without having to have any vendor lock-in so that's a kind of goal design parameter we always put in like how can we keep our design our tools and technologies in a very inter-agnostic format so I think that's a list like Katrina said it's kind of long list of things what we adopt from open source.
Absolutely there's a very great dependence as well as a support for open source within the team.
That's awesome, awesome. Raise your hand if you're hiring.
All right. He's got multiple openings. Let's go ahead and start with Nicole.
Tell us a little bit about what you're hiring for and kind of the core competencies and behaviors that are important for you to be successful on your team.
Yes so I am looking for at this time early talent which would be college graduates, people that are transitioning into analytics because I believe in the push me pull you mentality is someone pushed me into this opportunity so I want to be able to provide that to someone else where they can apply their academic training within the workforce and then I'm also looking for mid-level candidates which is someone that is about three years within the analytics domain.
If you're looking please go on the Klaffner jobs website and apply if you're interested in our roles.
Now what am I looking for in a data analyst? I definitely need someone that enjoys solving problems.
I need you to be a critical thinker where you're trying to put yourself in the business user's mind and not only address the use case or the problem but think about what other answers that you could provide to them like those what ifs.
Those what ifs will always turn into more what ifs so we can continue to enable our business to make data-informed decisions.
I also need someone that likes to tell a story with the data.
Someone that can tell the narrative and be able to flow but then also be able to iterate on that story as things change because the industry is going to change, the domain is going to change so you're able to iterate your story as that happens.
From a technical perspective as it was stated we do use Google Cloud so BigQuery is one of the primary skills that we need and I'll say SQL.
I'm not necessarily married to the tool but more so the skill set because you can get ramped up on the tool if you have the fundamental SQL skills and with the large volumes of data that we have there's no way in any possible way that we can do analysis in Google Sheets or Excel so those are skills that we definitely need.
As it pertains to visualization I definitely need data visualization skills.
We do use Tableau as our enterprise-wide tool but again I'm more concerned about the skill set than the actual tool because you can get ramped up on the skill.
In addition to that Python or R, preferably Python because we are a Python shop because as we continue to mature as a team we will be partnering a lot more with our data scientists and having that skill set would make that partnership go smoother.
And lastly just as it pertains to anything from the soft skills perspective I want someone that brings something different to the team.
Like of course you're going to have the fundamental and the core skills but my current data analyst team they are dynamic and they are fantastic but they all have a unique something unique about them that makes our team really gel.
It's kind of like a cake you can't use the same ingredient or it'll be nasty but if you have different ingredients that's what makes the cake delicious and that's how I look at the data analyst team.
I love that metaphor and if I were to give anybody advice who is applying for data analyst roles I think one of the shortcomings that people sometimes miss on their resume is they just kind of list the activities that they did, how those activities ended up showcasing results.
And so I think for data analysts you want to be able to kind of tile that in there and what that impact to the business as opposed to just a laundry list of activities that you did.
Yep thank you Todd for that.
Yes that's very true. You bet. Chandra tell us a little bit about your opening.
Yeah we've been always hiring like you know Todd you're the one that's always like with all this back and forth with hiring.
We are adding a mid-level data engineer as well as senior level data engineer so like based out of Austin.
Requires like strong data engineering skills using Spark, Scala.
So that's one of the key skills that we look for and then SQL is always there like that's a kind of a DNA of any candidate that we look for strong SQL skills.
And also the skill technique always like we like to think about resource when we look for is like how can they marry like technical skills with their interpersonal skills and then business development skills.
That's the focus we always do is like how they can combine those all three categories and bring in that kind new value to the team.
How they can bring in the new talent or capability to the team that the current team might need to develop further.
So those are the candidates we look for and then somebody who has done this in a scale because a lot of scenes probably see like no they've done this but have they done it in scale in a production level grade looking at from like end to end from perspective right starting from collecting data until business gets impacted and what kind of impact they've been able to make how they collaborate with different stakeholders how they even communicate their ideas to the team.
So that's big we can do a great work if we want to communicate the ideas so we look for those talents who cannot share the ideas and communicate and present to the executive team or broader audience.
So those are different talents we look and then also we have a balance between teams like we have a lot of senior engineers and new grads and new engineers join our team how they can be an effective mentor because that's what ways we try to put balances like being an individual contractor is great but are you being a great mentor a team player who can work with multiple teams because as Nicole and that's not saying like we are work as a team how we can work cross collaborate within the team and bring in that value and then take that to go as a one friend when you're representing the business when you stakeholder how that can be established.
So those are it's always a balance we look for like technical interpersonal skills soft skills and then business enablement skill how can they quickly on a domain and enable them to be a successful engineer.
Awesome awesome Katrina tell us about your openings.
Yeah absolutely so we are hiring currently for both machine learning engineers as well as data scientists and we are looking for more mid-level people and some you know entry level people with great potential so that's the area that we're really trying to focus on right now.
I want to say all of that that was mentioned right right we want all of those things but we also really within the data science team of course we want a really strong background and fundamental knowledge in data mining machine learning and also visualization techniques right.
What we're really trying to do right is to democratize our data and make this less scary the idea that we have these automated systems on the back end that are able to you know produce insights and to help with decision making and to automate decision making and things like that you know really work with our stakeholders to make sure that we are providing that business value but then also making sure that it's usable and that it's also very you know accessible right we want people to you know interact with the data and get very very deep within it.
So that also leads into the communication skills that were really alluded to by both Nicole and Chandra here right that on the data science team it's very important for us to be able to not only you know quickly learn a domain right but also be able to communicate with our stakeholders make sure that we can speak in that you know business value way but then also be able to turn around and be able to work with our engineering staff and be able to have that you know engineering background and and vocabulary in order to be able to actually you know actualize these systems right because we're really trying to unlock this technology in order to make it more accessible to the people who need this data and need these these insights in order to move forward with their jobs.
Nicole, maybe you can share an example of a challenge that the team has either already solved or is currently solving that would be exciting for people that are looking for new opportunities.
How much we have eight minutes?
Okay. So the first thing I'll say is the ultimate goal of the challenges we're trying to address is we want to be able to provide self-serve for our business users.
So some of the things that we are collectively working through as a team is really being able to understand the behaviors and patterns of our customers and as they journey through Cloudflare because we want to be able to enable just as an example the marketing team with who they target for the marketing campaigns and they're not just generalizing a target or their sales team as they're engaging with our customers.
They can have a more customized discussion because they have the data and insights at their fingertips with just to say our infrastructure team helping them understand how traffic is changing over our network so they can do server capacity planning in a more data-informed way because I always say the infrastructure team is like the nucleus of Cloudflare.
Without them we are done.
There is no Cloudflare. So we really need to be able to have our network up and running optimally.
And then also helping our executives understand the health of the company but also working through what success looks like and what those metrics are and how that may change over time.
So those are just some of the a few things that we're working on and it's just the beginning and we're just going to continue to iterate on that.
That's awesome. That's awesome.
Chandra, tell me about something that you've learned during your time at Cloudflare that you think will carry with you the rest of your career.
Yeah that's a kind of there's a lot of it's very interesting journey.
Everybody like says like a lot of learnings from Cloudflare.
First like in level of scale that Cloudflare handles like you know the level of products and new services that we deliver in a space like unimaginable like when we say like a lot of people have been saying Cloudflare is a pure engineering company.
We can really feel it like how Cloudflare being that kind of culture they like how quickly they iterate an idea and deliver a product and which is enabled for a wide variety of customers like across all margin across geos.
That gives us a great motivation to see like how Cloudflare has been fast-paced and doing that in a scale and with such a vigor.
So that's kind of what we feel like okay how we can do the same thing like you know how we have a unique opportunity cross collaborative so many business partners.
We have a unique opportunity kind of doing at a scale like you know because a lot of data driven enabling capability we can enable for our customers and we have a unique opportunity to kind of collaborate at that level and this opportunity kind of gives that scale where we can do with different use cases whatever things we want to enable our partners to reach that level.
That is kind of giving it more like you know with the amount of data and level of sophistication we do with data science models and critical insights that Nico's team is doing how that can be enabling making a difference giving that from ground up that's what we're doing right.
BI platform was built from ground up that's what the previous sessions we did all were done and how we were able to do in a short time it's all collaboration with different business partners that we could get it happen and because the culture of Cloudflare is always collaborative and transparent which is great helping us to further expedite that enablement that we're able to give the business which is a great experience for me to carry forward and which will be like continue to grow as I spend more time in Cloudflare.
That's awesome that's awesome Katrina how about how about you something that you hadn't had the exposure to or done before in your career that you know that what you'll carry with the rest of your career?
Absolutely I do want to reiterate what Chandra just said the scale is unbelievable here just the sheer volume of data that Cloudflare produces and you know it's really quite humbling to tell you the truth it's and so that's for sure something that we I will for sure take that forward in my career.
I also just want to mention that there is a great opportunity I think within you know data science is maybe a little bit newer and a little bit more foreign to people that we're working with right so that opportunity to educate to be able to work with people to show capabilities to be able to really provide some thought leadership and how we might want to move into machine learning and artificial intelligence moving forward has definitely been an incredible opportunity for me but also I think you know it's taught us a lot about you know going back to what we were saying about making this accessible and maybe making it a little less scary right for people to understand this process a little bit better.
I think that that's for sure something that I'll take it take with me as I move forward.
That's awesome. Nicole?
Yeah I'll keep it short what Katrina and both Chandra said but also the fact that we're working through ambiguity so I would say keeping it simple and making sure you're not just looking for the new and shiny technology but keeping it simple as it applies to the need so that we can push out what what the product is for our end users and that's something that I'll take with me just in general is don't look at the new and shiny thing but look at what is practical and what is simple to create the solution.
That's awesome. That's awesome and in terms of just getting to know each of you you know a little more on a personal level when you're not working what are what are things that you do to keep yourself busy and entertain?
Nicole why don't you start? I have a pretty simple life. My family is my everything so number one taking care of them.
Other than that I love reading so definitely have my reading list for the year and enjoying my back patio before it gets too hot outside in the Texas summers.
I got into the home gym craze when the pandemic hit so that's one of them but that's pretty that's pretty much it for me.
Yeah awesome. Chandra 30 seconds same question. Yeah for me it's more often I just started biking so last summer I had a success in teaching my daughter to learn biking so this summer I signed up for teaching my son to ride the bike you know so it's a he's four-year-old but I know it's going to take some time but I hope I'll make it quick for him like because that helps me to bike with him otherwise I've been walking with him so far so I want to start biking with him together.
Katrina bring it some. Sure I actually spend quite a bit of time with non-profit organizations doing data science in my spare time to tell you the truth so I'm involved.
I talked a little bit about Numfocus earlier at least alluded to them but that's a scientific software open source community.
I also work with being able to help with some of the gerrymandering issues that we have here in Texas as well as helping out with human trafficking problems that we face all around the world and then as far as beyond that I'm also a aspiring writer.
We're out of time and we may or may not go live but thank you so much and if you're watching reach out to any of us on LinkedIn if any of these opportunities resonate with you.
Thanks so much for joining. Thank you. Thank you, Todd. Thank you.
Have a good one. Bye-bye.