Bluebird

As Managing Partner IT at Bluebird Recruitment, I am responsible for building out our IT recruitment team with ex-industry experts and connecting top IT talent with leading companies throughout Europe. 

We focus on various IT areas, from software development, cloud & infrastructure, data science & engineering, product roles and more. 

Determined to transform the recruitment space, my goal is to integrate authentic industry expertise, fundamentally changing the sector. By utilising my technical background and understanding of the industry, I am committed to making an impact.

Managing Partner IT

ASML

At ASML, a leader in the semiconductor industry, I contributed to developing a platform designed to provide insights into machine operations. This platform played a crucial role in monitoring for errors, facilitating timely fixes, and employing artificial intelligence to anticipate potential failures.

Due to the proprietary and sensitive nature of the technology and processes at ASML, much of the detailed information about my role and the project specifics is classified. This experience has honed my skills in handling confidential data and working within strict operational security guidelines, reinforcing the importance of discretion and integrity in high-stakes environments.

𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆: 𝗦𝗲𝗺𝗶𝗰𝗼𝗻𝗱𝘂𝗰𝘁𝗼𝗿

𝗧𝗲𝗰𝗵 𝘂𝘀𝗲𝗱: 𝗝𝗮𝘃𝗮, 𝗔𝗻𝗴𝘂𝗹𝗮𝗿, 𝗔𝗶𝗿𝗳𝗹𝗼𝘄 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴.

Senior Fullstack Engineer

Alphabet Nederland

At Alphabet Lease (part of BMW Group), a leasing company, my role involved developing modular components for a complex system and leading several frontend projects. As a Chapter Lead I spearheaded technical innovation within our eco-system. This included conducting Angular upgrades and enhancing the codebase for better performance. The frontend team, comprising 10 members, was responsible for overseeing and managing 20 separate Angular projects, each an individual application.

I also contributed to the refactoring of a key application, transitioning it to a monorepo structure with Nx to improve maintainability and scalability. 

𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆: 𝗔𝘂𝘁𝗼𝗺𝗼𝘁𝗶𝘃𝗲

𝗧𝗲𝗰𝗵 𝘂𝘀𝗲𝗱: 𝗔𝗻𝗴𝘂𝗹𝗮𝗿, 𝗝𝗮𝘃𝗮, 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀

Frontend Chapter Lead

Lyfo

At Lyfo, a provider of advanced mobile coverage solutions critical for ensuring reliable communication, I developed an Angular application made for configuring the backend for their clients, including the Dutch police. The code that was written is quality tested by an external party and was found to be excellent.

𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆: 𝗧𝗲𝗹𝗲𝗰𝗼𝗺 (𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲)

𝗧𝗲𝗰𝗵 𝘂𝘀𝗲𝗱: 𝗔𝗻𝗴𝘂𝗹𝗮𝗿, 𝗚𝗼, 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀

Lead Frontend Engineer

Datlinq

At DatLinq, a Dutch company known for its data solutions in the foodservice industry, I worked on the SaaS platform (DOX). My responsibilities included the development and enhancement of Data Outlet X and Salesmapp API. This platform gave our clients in the foodservice market data-driven decision making capabilities.

𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆: 𝗦𝗮𝗮𝗦 & 𝗙𝗼𝗼𝗱𝗦𝗲𝗿𝘃𝗶𝗰𝗲

𝗧𝗲𝗰𝗵 𝗨𝘀𝗲𝗱: 𝗔𝗻𝗴𝘂𝗹𝗮𝗿 𝟴+, 𝗝𝗮𝘃𝗮, 𝗚𝗼𝗟𝗮𝗻𝗴, 𝗡𝗼𝗱𝗲, 𝗚𝗿𝗮𝗽𝗵𝗤𝗟

Lead Fullstack Engineer

Ministerie van Volksgezondheid, Welzijn en Sport

Increasing mobility of citizens between EU countries results in a growing number of people seeking healthcare outside their own region or country. To facilitate the exchange of patient data among countries, the European Union collaborates with member states on an international infrastructure known as eHealth Digital Services & Infrastructure (eHDSI). The Netherlands is integrating into this system of national contact points with its own national contact point called the National Contact Point for eHealth (NCPeH). Through the NCPeH, healthcare professionals can digitally retrieve patient summaries from patients in EU countries requiring unplanned care in the Netherlands.

𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆: 𝗚𝗼𝘃𝗧𝗲𝗰𝗵

𝗧𝗲𝗰𝗵 𝘂𝘀𝗲𝗱: 𝗔𝗻𝗴𝘂𝗹𝗮𝗿, 𝗝𝗮𝘃𝗮, 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀, 𝗢𝗽𝗲𝗻𝗡𝗖𝗣.

codecentric Nederland B.V.

Codecentric is a consultancy firm. Since the start I have worked on one project:
Project: MOJO (Read below)

Fullstack Engineer

Mojo Concerts

At MOJO Concerts, a leading promoter of live music and entertainment events, I was tasked with developing an application named UNA. UNA is designed to streamline event access control, scheduling, and workflow management, playing a critical role in enhancing the operational efficiency of live events. This application utilizes a modern tech stack and leverages cloud-based solutions to ensure scalability and reliability.

𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆: 𝗦𝗮𝗮𝗦 𝗳𝗼𝗿 𝗮𝗰𝗰𝗲𝘀𝘀 𝘁𝗼 𝗲𝘃𝗲𝗻𝘁𝘀 & 𝗰𝗼𝗻𝗰𝗲𝗿𝘁𝘀

𝗧𝗲𝗰𝗵 𝘂𝘀𝗲𝗱: 𝗔𝗻𝗴𝘂𝗹𝗮𝗿, 𝗡𝗼𝗱𝗲, 𝗚𝗿𝗮𝗽𝗵𝗤𝗟, 𝗖𝘆𝗽𝗿𝗲𝘀𝘀, 𝗚𝗖𝗣, 𝗔𝗪𝗦

Working while being a student

During my studies, to gain practical experience, I wanted to work. I faced the challenge that no company was willing to hire me part-time. Driven by resilience and determination, I didn't let this stop me. Instead, I chose to start my own freelancing 'company' as a way to gain that experience. I've worked on a variety of projects, ranging from small websites for local artists to large-scale applications like a planning system for a major transportation company. This experience has given me a versatile skill set, making me broadly deployable in various IT roles.

The tech stack I've used includes: WordPress, PHP, Laravel, JavaScript, AngularJS, Angular, React, HTML/CSS, and React Native.

As a former developer, I redefine the kind of developer you think you’re looking for and see beyond the resume to ensure you hire the real deal.

If you're a developer or hiring manager, feel free to send me a message on work related things.

If you're a human being you can always send me a message with anything random because I like engaging with you nonetheless 😉

🚀 Dennis van Valderen

Founder @ Drilldown | Custom-designed business dashboards for B2B SaaS that deliver insights in one click, eliminating the need for complex spreadsheets in just 60 days.

We help B2B SaaS teams build an owned data system - so metrics align across the organization and growth decisions are based on evidence.

This might resonate:

As B2B SaaS companies scale, it becomes harder to understand on where growth comes from and what to do next.

Most teams already have dashboards, data integrations, and the technical ability to build new ones. 

Yet they still struggle to agree on what metrics mean, explain drivers of revenue, or produce reliable forecasts.

What you need is one set of accurate, explainable numbers that your team can rely on.

Our work focuses on the data system underneath dashboards: the definitions, structure, and logic that determine whether analytics can support decision-making at scale.

If you’re trying to make decisions based on data but the numbers don’t line up, this explains why: https://thomasvarekamp.substack.com/p/you-have-dashboards-why-are-decisions

Thomas Varekamp

I used to love Power BI. 

Now I hate it.

Not because it’s a bad tool. But because of how most teams use it.

Power BI lets you build dashboards.

But to build dashboards, you need to model the data first. And that’s the trap.

Most people do datamodelling inside the BI tool. That’s a huge mistake.

Why?

Because as your business grows, your data grows too.

What starts as a simple report becomes a spiderweb of logic.

- impossible to trace.
- brutal to maintain.
- easy to break.

Here’s what to do instead:

1. build a proper data warehouse.
2. manage your single source of truth there.
3. use Power BI only as a presentation layer.

A good warehouse gives you:

- version control → roll back mistakes.
- traceability → find and fix issues fast.
- quality checks → know the data is right. 

When you separate logic from visuals:

- people trust the numbers.
- teams make faster, better decisions.
- you don’t have to rebuild everything later.

It’s cleaner, smarter, cheaper.

Don’t duct-tape your data stack.

Build it right from the start.

Drilldown | a data science team in your pocket for B2B SaaS.

Data teams will look completely different in 2 years.

The standard data analysis workflow used to work like this:

1. you have a question.
2. you ask the analyst.
3. you wait a week.
4. you get a report.

And then you figure out if you actually got what you wanted.

Now?
1. you ask the question.
2. you get the answer in seconds.

Think of this:

Marketing can ask “what is our best channel?”
…without opening 20 dashboards.

The CEO can ask “where to focus next quarter?”
…without going through slide decks.

Sales can ask “which deals need attention?”
…without digging through the CRM.

Everyone will be able to use business insights, instantly. 

So what will data teams do? 

Their primary value no longer comes from structuring and analyzing data. 

It will come from:
→ building foundations AI can understand.
→ helping others formulate questions.
→ translating insights into actions.

The titles will have to catch up with the work.

Business intelligence is being commoditized way faster than anyone realizes.

Workflows that used to require a team of 5 are now being handled by 1 marketer using Claude Code.

Here is exactly where BI is heading in the next 12 months:

1. 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗮𝗻𝗱 𝗱𝗿𝗮𝗴-𝗮𝗻𝗱-𝗱𝗿𝗼𝗽 𝘁𝗼𝗼𝗹𝘀 𝗮𝗿𝗲 𝗮 𝗱𝗲𝗮𝗱 𝗲𝗻𝗱.

→ using code and letting AI handle it is much faster, cheaper, and robust. 

Learning Power BI now? You're in for a big surprise.

2. 𝗦𝗽𝗿𝗲𝗮𝗱𝘀𝗵𝗲𝗲𝘁𝘀 𝗮𝗿𝗲 𝗼𝗻 𝘁𝗵𝗲𝗶𝗿 𝘄𝗮𝘆 𝗼𝘂𝘁 

→ no one will manually update cells and write formulas in Excel. Just hook your data to AI and ask for the insights.

3. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝟵𝟵% 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 

→ AI will do the heavy lifting. Humans will transition from "analysis" to "validation", only checking outputs.

4. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗴𝗲𝘁𝘀 𝗼𝘂𝘁𝘀𝗼𝘂𝗿𝗰𝗲𝗱 𝘁𝗼 𝗔𝗜 

→ machine learning is code-heavy. AI writes code perfectly. Humans will just set the framework. AI does the rest.

5. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗮𝗻 "𝗲𝗮𝘀𝘆" 𝗷𝗼𝗯.

→ AI can now auto-generate connectors, map schemas across sources, and self-fix broken pipelines in real-time. 

So, where is this going?

I think we will see companies either:

1. hire a single data person & upskill employees.
2. have external agencies handle it.

(or both)

Just as computers replaced most mathematicians, AI is doing the exact same thing to BI.

Only this time 1000% quicker.

If you disagree, you simply aren't paying attention to what's happening right now. 

I'm shocked how fast things are progressing.

Expandi hit $10M ARR bootstrapped. Here’s the data system we built to help them grow faster.

From the outside, Expandi was an unstoppable force:

→ 0 to $7M ARR in under 3 years.
→ fully bootstrapped.

But as they scaled, internal problems were growing.

- marketing couldn't measure LTV. 
- finance struggled to reconcile $ vs. € ARR. 
- teams burning hours updating spreadsheets.

We partnered with them to install The SaaS Intelligence System.

The execution plan:

𝟭. 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘇𝗲

We identified growth blockers and defined clear goals for every department. 

No vanity metrics.

𝟮. 𝗕𝘂𝗶𝗹𝗱

We implemented complex ARR logic (New, Expansion, Churn) into a centralized warehouse they own.

𝟯. 𝗔𝗱𝗼𝗽𝘁

We put the whole team (Marketing, Customer Success and Finance) on shared metrics. 

Before: no realtime insights.
After: trusted metrics for decision-making.

Swipe to see how we built their data system.

How we built Expandi's data system

4 deals in pipeline. 2 calls. 

A 7-day update:

I spent a chunk of the week experimenting with Claude Code, and holy shit I'm impressed.

A single person can now easily fulfill all the roles of a data team.

Data work will look very different in 12 months. 

𝗢𝗙𝗙𝗘𝗥 𝗨𝗣𝗗𝗔𝗧𝗘:

Due to these new insights, I'm testing out new things we can do for our clients.

Technical obstacles are pretty much a thing of the past.

My idea is to put more focus on advanced data science work.

Meaning: building custom prediction models (churn prediction, ARR forecasting, etc.)

This used to take ages to build & validate, but not anymore. 

𝗪𝗘𝗘𝗞𝗟𝗬 𝗦𝗧𝗔𝗧𝗦:

Deals:

- 4 potential deals in pipeline.

Calls (7 days):

- 1 call with a previous client.
- 1 call with a new lead.

Substack (7 days):

- 24 new views.
- 1 new sub.

(will publish new article soon)

Linkedin (7 days):

- 40,259 new impressions.
- 74 new followers.

Let's see if we can improve this week 👊

We are entering the era of the 1-person data team.

I spent the past week testing AI data agents. It made me to rethink everything:

- data science.
- data analysis.
- data engineering.
- analytics engineering.

AI can now execute all of it.

Which makes one thing very clear: most data tools with buttons are going to fizzle out.

Why pay for a rigid analytics platform when an AI can write, refactor, and maintain the underlying code directly?

My predictions:

1. 𝗖𝗼𝗱𝗲-𝗵𝗲𝗮𝘃𝘆 𝘀𝘁𝗮𝗰𝗸𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗱𝗼𝗺𝗶𝗻𝗮𝗻𝘁.

AI makes raw code the fastest, cheapest, and most robust way to build data systems. 

Writing and maintaining SQL, Python, and infrastructure is no longer the bottleneck.

2. 𝗗𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗰𝗼𝗺𝗺𝗼𝗱𝗶𝘁𝘆.

Moving data from A → B is a solved problem. 

Just give the API docs to an AI agent and ask to build the pipeline. 99% of the time it will one-shot it.

(try Claude Code if you don't believe me)

3. 𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝘄𝗶𝗹𝗹 (𝘀𝘁𝗶𝗹𝗹) 𝗯𝗲 𝗸𝗶𝗻𝗴.

AI can write the code, but it doesn't know your complete business context. 

Deciding what to model, why it exists, and how it should be interpreted remains a human responsibility.

The next 12 months are going to be VERY interesting.

The data analyst role we know won’t exist in 3 years.

And that’s a good thing.

For years, analysts were valued for one output: dashboards.

Pretty charts, nice filters and providing a surface-level view of the business.

That is ending.

Agentic AI is changing how analysts work completely. 

They’re not just dashboard builders anymore. They’re end-to-end solution developers.

That means:

1. defining business logic up front.
2. modeling data to drive strategy.
3. delivering insights without 5 handoffs.

And yes, way fewer data people will be needed.

But the ones who remain?

They’ll have more trust, and more impact.

The analyst isn’t just the “report person” anymore.

They’re becoming orchestrators behind the insights.

In 2-3 years, all data teams will become AI teams. 

Here's why:

Data teams today spend countless hours in meetings explaining dashboards and writing SQL. 

Most of this work doesn't add real value.

AI can do this faster, better, and without human bias.

In the next few years, analysts will stop spending time on dashboards and answering questions.

They will be maintaining infrastructure for AI:

- designing data models.
- adding context to data.

Here's how that works:

1. business software generates data.
2. data warehouse ensures high quality data. 
3. AI pulls insights and executes tasks.

Data will be the most valuable resource in every organization. 

This will be the source of truth for AI.

taplio

The Battle

🚀 Dennis van Valderen

Thomas Varekamp

Followers

Posts

Engagements

Likes

Performance ScoreThomas Varekamp's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

🚀 Dennis van Valderen

Thomas Varekamp

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Thomas Varekamp's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreThomas Varekamp's LinkedIn Performance