As an Independent Data & Technology Consultant, I help businesses unlock the full potential of their data - transforming it into actionable assets that drive efficiency, innovation, and measurable value. 

Here are some ways I can help you:
- Conceptualizing and implementing business metrics end-to-end
- Modelling the data in an easy-to-use way so it can be consumed by different functions in the organisation
- Setting up data infrastructure
- Building a data ingestion layer for multiple sources
- Platform migrations
- Assessing and identifying gaps in your current data platform and analytical capabilities
- Improving the accuracy of your analytics by improving Data Quality and Governance

About me
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Highly accomplished Data Professional with a decade of experience driving innovation in academia, startups, and tech giants like Meta. My journey began in Geodesy and Geoinformatics (MSc), evolving from GIS and Remote Sensing to Software and Data Engineering.

Proven track record as both Individual Contributor (IC) and Manager, leading high-performing teams and architecting scalable solutions. Passionate about harnessing data to drive impact. Committed to mentoring and growth.

At Meta, I led Data Engineering teams responsible for the backend ranking systems that power ads across all platforms. I built and scaled the London team from inception, growing it from 0 to 9 engineers. My primary focus was leveraging data to unlock incremental business value and establishing robust analytical measurement foundations.

Outside work, I cherish time with my loving family - my wife and precious daughter.

Strategic observation: It’s actually surprising that we are getting any data analytics at all!

Think about the three parties involved...

Producers:
- People or processes that “produce” the data. 
- Unless they use it for something else, they don’t care about the data they produce. 
- They are not incentivised to even be aware of what happens with the data downstream.

Platform:
- Engineers and their platform that supports ingesting, processing, and managing all the data. 
- They are like librarians. They care about the library, how books are stored, that people rent those books, and keeping them accountable. 
- But they have zero understanding of how the books are used. That’s “not their business”. They just provide the books. Often low-quality garbage, but who cares - people still read that stuff.

Consumers:
- Finally, our dear internal analytics and business users. 
- They have the most business context. They want data, they know how it should look, they care about quality. They literally drive the value out of the data. 
- All that talk about data being the asset of your company can’t be realised without them. 
- But they can’t do much. They rarely know where the data is coming from or its quality. They need to operate in ambiguity and are often left without help. 
- They keep begging for support and are unheard or misunderstood.

Don’t get me wrong, all these parties do important jobs and work hard. It’s just that their way of operating, goals, and incentives are misaligned. Don’t hate the players, hate the game.

But I think there is hope. A common ground and incentive that can help. It’s not “data” that should unite all of them. It’s helping their business. Under the hood, they all work towards a singular goal: to help their business thrive. That should be their beacon.
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👋🏼 I'm Slawomir. I guide companies at any stage to turn data into clear action and real business impact.
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Slawomir Tulski

Data Leadership Consultant | ex‑Meta | Fixing Underperforming Data Teams

Change is hard in general. It’s even harder when it’s the organisation that has to change.

- There are people who are used to the way they work.
- The company is there, so whatever exists right now... it kind of works.
- We may find out that we or someone else are to be blamed.
- New things are uncomfortable and we have to learn.
- Tons of other things.

The moral? You better have a good motivation. There are some obvious ones:
- Burning platform
- Wasted money
- People leaving en masse
- Fear of missing out on massive opportunities

If you have those, use them - but sprinkle in some positive aspects so it's not purely negative motivation. A good balance between fear and reward works far better than relying solely on a negative approach.

If you don’t have anything like that, it's better to find strong motivation elsewhere. Organizations don’t change without a compelling reason. Simply having a new tool or process isn’t enough - that alone isn’t true motivation.
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👋🏼 I'm Slawomir. I guide companies at any stage to turn data into clear action and real business impact.
♻️ Repost to help your network.

Data Engineering is often a "black box" for stakeholders.

If you're wondering whether your data engineers are doing solid work or a house of cards, look for these 5 signs. 

You don't need to be a technical person to sense if something is wrong.
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👋🏼 I’m Slawomir. I help established businesses move data from a cost center to a value driver.
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Stop guessing if your Data Engineering quality is "Good"

A new structure isn’t a cure‑all. It’s a start, not the solution.

Centralised top-down approach: I’ve got a ticket to build a dashboard. It's a rather stupid idea, and the problem could be solved in a better way. But who am I to question it? They pay me to do the tickets.

Data-mesh approach: My PM asked me to build this dashboard. Not sure why they even need it… but hey, who am I to question the PM? They know their stuff. Also, I need feedback from them later on for my performance assessment.

Company culture is a tricky thing. On one hand, it's a catch-all, often meaningless phrase we throw around in random contexts. On the other hand, it’s a real force that impacts everyone in the company.

It’s hard to see it objectively from the inside or to measure it. It’s even harder to change it. But it can silently kill any process, structure, or “best practices.”
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👋🏼 I'm Slawomir. I guide companies at any stage to turn data into clear action and real business impact.
♻️ Repost to help your network.

Data Engineering is complex, but it shouldn't feel like rocket science.

When adding a single column or tweaking a metric becomes a "mini-project," your ROI is leaking. Inefficiency usually hides behind excuses about pipelines and source systems, but the root cause is often process-driven.

Here are the 5 symptoms of a data team that has lost its agility - and how to spot them without being an engineer.
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👋🏼 I'm Slawomir. I help companies fix these exact bottlenecks and drive real business impact.

Which of these points hits home the most? If you’re ready to solve it - DM me or drop a comment. ♻️ Repost to help your network.

Why "Simple" Data Requests Take Weeks

Rejection isn’t the end. It’s a redirection.

Most people fear rejection. It’s wired into us. Instead of diving into the psychology behind it, I want to look at it through a leadership lens.

A “Yes” and a “No” work the same under the hood. They’re both feedback signals shaping our direction. Each one closes some doors and opens others. Of course, in the moment we want “Yes,” while the “No” doors feel like setbacks.

Early in my career, I applied for a paid internship in Software Engineering. I failed the Bash test and didn’t get the role. At the time, it felt like a door slamming shut. Years later, I’m grateful it did. That path would have led me into QA Testing - an important discipline, though not the one aligned with who I am or the work I’m meant to do. That “No” pushed me toward a trajectory I’m proud of today.

Rejection stings in the moment. With distance, many of those moments reveal themselves as turning points. Some even become gifts.

Leaders grow when they learn to hold that perspective - for themselves and for the people they guide.
—-—
👋🏼 I’m Slawomir. I help established businesses move data from a cost center to a value driver.
♻️ Repost to help your network.

High‑performing data teams aren’t accidental. They’re built on tiny rituals that compound quietly in the background.

Here are 5 team rituals that will boost your data engineering team’s performance in the long run 👇
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👋🏼 I’m Slawomir. I help established businesses move data from a cost center to a value driver.
♻️ Repost to help your network.

5 Rituals for Elite Data Engineering Teams

“Why the heck did we build it this way? It’s unnecessary… It makes our team’s life harder…”

No satisfying answer. Until one conversation with a very tenured employee.

Bingo.

But let’s start from the beginning.

Over the years, I’ve learned that there’s always a reason why something is done a certain way.

A simple example: you look at a solution, setup, or workflow and at first you just can’t understand why on earth it was designed like that.

Dig deeper with curiosity and things start to make sense. And with that understanding, you usually learn something important.

At a high level, it works like this:
If you’re lucky, the organisation is well-run, has strong talent, and makes thoughtful leadership decisions. In that environment, solutions (platforms, functions, models, etc.) tend to be clean and structured. Most complexities come from technical constraints or the nature of the problem itself.

More often, though, the corporate world is far from perfect. There’s historical baggage, uneven talent distribution, leadership mistakes, and countless external factors. Engineering teams have to build around all of it.

Even “greenfield” projects are never as greenfield as they look. They’re built on the technical and cultural foundations your org has accumulated over the years. You can’t ignore that context - and knowing it helps you operate far more effectively.

Learn more about your company’s history, culture, past decisions, and aspirations. It will help you:
- Question the status quo
- De-risk change
- Spot efficiency gains
- Collaborate more easily with others

Oh! I almost forgot… The answer from the story at the beginning.

“Our analysts used to work like this. We wanted to give them exactly the same feeling on the new platform.”

Context isn’t a luxury. It’s a leadership skill.
👋🏼 I’m Slawomir. I help established businesses move data from a cost center to a value driver.
♻️ Repost to help your network.

Is the Data Team closer to the Business or Tech? Why is that even a question? Can’t we just accept that it has its own place in the ecosystem, which is neither pure business nor pure technology?

The truth is, until the Data Team builds its position as a unique bridge with a distinct set of strengths and skills, it will be thrown to either side. In some places, it will be part of Engineering. In others, it will be spread across business-related departments.

Of course, it’s the job of senior leadership to champion and help position the Data Team, but the team has to do its part as well. How? Build connections and relationships. Proactively prove value. Build credibility and educate people instead of expecting that they should already know.

It’s a lot of work. But it’s worth it.

“If you build it, they will come” does not work in the world of Data and Analytics.
—-—
👋🏼 I'm Slawomir. I guide companies at any stage to turn data into clear action and real business impact.
♻️ Repost to help your network.

Most professionals who’ve worked with data - or even just read about it - know that siloed data is bad. But what about a siloed perspective?

You’re putting yourself in a disadvantaged position if, as a data leader, you cooperate mostly within the analytics department and maybe a narrow group of business stakeholders you feel comfortable with.

If that’s the case, you have to talk to more people. You have to understand a broader perspective and different points of view. You’ll be surprised how many pain points and opportunities are out there that you can’t see right now.

Real-world example. I talk to the Data Team and the picture looks relatively good. Things are just fine. Sure, there are small gaps here and there, but nothing major. Projects are well scoped and prioritized. No low-hanging fruits, as they did the groundwork in the past.

Then I talk to the business users and leadership—and the picture is completely different. Data is hard to work with and access. People wait weeks for new data integrations. There are coverage gaps.

Same company. Different perspectives. Where is the truth? Why is there such a disconnect?
—-—
👋🏼 I’m Slawomir. I help established businesses move data from a cost center to a value driver.
♻️ Repost to help your network.

A bold Data Vision is essential. I’m afraid it’s not enough though - dreams won’t save you.

If you want real progress, you need more than an inspiring vision. You need a tactical plan. One that’s grounded in today’s reality while still aligned with tomorrow’s ambition.

We all know that in today’s short‑attention‑span world, people stay excited about the big picture only for so long. But the vision takes time to materialise. And during that time, enthusiasm fades, trust erodes, and delivery feels slow.

To make things worse, business‑as‑usual keeps pulling you back into the chaos you’re trying to escape.

That’s why a tactical plan becomes your anchor.

It guides the day‑to‑day, protects momentum, and creates the small, meaningful wins that keep everyone engaged until the future arrives.
—-—
👋🏼 I'm Slawomir. I guide companies at any stage to turn data into clear action and real business impact.
♻️ Repost to help your network.

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