Fabian Werkmeister

I help you to become fluent in Data and enable you to translate between business & tech ★ Data Scientist @FUNKE ★ 🐍Python 🤖Data Science 🛠️Data Engineering

We are swimming in data.
Yet decisions are still based on gut feeling.
I build systems that close this gap ⬇️

I didn't start in tech, but switched over from economics.
That made me become a data engineer, who understands both business and data very well.
I don't just build pipelines, I build actionable data systems on the Google Cloud Platform that help you decide what to do next.

Here's what you can expect working with me:
1. Business First
Coming from a business background, I speak your language. I use simple and GCP-native setups, keeping your costs low and maintenance easy. My success metric isn't lines of code written, it's how fast I can help you make good decisions based on your data.

2. No Black Boxes
I don't build for you, I build with you. My process is "White-Box," meaning nothing is hidden and you will be involved from day one. All done within in a transparent and communicative partnership. 

3. Engineering with Heart
I believe technical projects fail due to human friction, not because of technological issues. I bring reliability, empathy and a huge passion for my craft to actually solve your business problems.

If you are looking for a Senior GCP Data Engineer who combines technical depth with business clarity and a genuine, transparent partnership  - let’s talk!

👉 Book a free call with me (you can find the link below) to see if we are a match.

Data Scientist

Growth only happens outside of your comfort zone.
And this year I'll be stepping 𝘄𝗮𝘆 outside of mine!

What began as a random idea during lunch last year turned into a company with my wife Daniela Werkmeister
Introducing: 𝗪𝗲𝗿𝗸𝗺𝗲𝗶𝘀𝘁𝗲𝗿 𝗟𝗮𝗯𝘀 🚀

As of today, I am available as a 𝗙𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗲𝗿 𝗮𝗻𝗱 𝗖𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁.

𝗪𝗵𝗲𝘁𝗵𝗲𝗿 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝗮𝗱𝘃𝗶𝗰𝗲, 𝘀𝗵𝗼𝗿𝘁-𝘁𝗲𝗿𝗺 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝗼𝗿 𝗵𝗲𝗹𝗽 𝘄𝗶𝘁𝗵 𝗲𝗻𝘁𝗶𝗿𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀, 𝗵𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗰𝗮𝗻 𝗱𝗼 𝗳𝗼𝗿 𝘆𝗼𝘂:
✅ GCP Data Engineering & Architecture
✅ Data Integration & Modeling
✅ Data Warehouse & BI Reporting
✅ Data Strategy & Architecture Consulting

You get technical excellence delivered with a huge passion, a lot of ❤️ and the right pinch of humour :)

Because in the end, data projects are about people working together to solve problems.


I am ready to build something amazing with you!

𝗪𝗵𝗲𝗻𝗲𝘃𝗲𝗿 𝘆𝗼𝘂 𝗮𝗿𝗲 𝗿𝗲𝗮𝗱𝘆, 𝗯𝗼𝗼𝗸 𝗮 𝗳𝗿𝗲𝗲 𝗰𝗮𝗹𝗹 𝘄𝗶𝘁𝗵 𝗺𝗲 𝗵𝗲𝗿𝗲:
https://lnkd.in/eNy-gb8j
(you can also find the booking link on my profile)

I build actionable data systems on GCP that drive decisions | Technical excellence delivered with passion & ♥️ | Freelance Data Engineer & Independent Consultant

Your most powerful skill in data is not Python or SQL.
𝗜𝘁’𝘀 𝘁𝗵𝗲 𝗰𝗼𝘂𝗿𝗮𝗴𝗲 𝘁𝗼 𝗮𝘀𝗸 𝗯𝗮𝘀𝗶𝗰 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀.

I used to sit in meetings with business and sales
Nodding along when "revenue" was thrown around
Definitely didn't want to look like I don't know the basics

One day, I actually had to recompute this column
I noticed that I had no idea how to approach that
Felt embarassed to ask, since project start was long ago

But I couldn't continue, so I just asked in the next meeting:
"Can we briefly define 'revenue' in this specific context?"
Long story short - got the best explanation ever 😅

Expected to get an epic roasting
Indeed got several 1 on 1 deep dives
Just by getting involved and asking questions!

𝟯 𝗘𝗮𝘀𝘆 𝗦𝘁𝗲𝗽𝘀 𝗵𝗼𝘄 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼 𝘁𝗵𝗶𝘀:
1️⃣ Play the beginner and ask fundamental questions
2️⃣ Listen carefully and 𝘄𝗿𝗶𝘁𝗲 𝘁𝗵𝗲 𝗮𝗻𝘀𝘄𝗲𝗿𝘀 𝗱𝗼𝘄𝗻!
3️⃣ Try to keep your tech jargon on a basic level, too


It feels uncomfortable to ask basic things.
But there is something much more uncomfortable:
➡️ 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝗮 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝘁𝗵𝗮𝘁 𝗻𝗼 𝗼𝗻𝗲 𝘁𝗿𝘂𝘀𝘁𝘀


𝗣𝗦: next level is to ask 3 different departments and align their vastly different definitions of "revenue! 😜

---

Hi, I'm Fabian👋
I write about actionable data systems on the GCP
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𝗔𝗺𝗮𝘁𝗲𝘂𝗿: "I build complex data platforms"
𝗣𝗿𝗼: "I talk to stakeholders"

I used to think my job as a data engineer was to stay quiet and build the most sophisticated solution possible.

I thought business context was just random noise.

𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁?
❌ I built complex solutions for problems that didn't exist.
❌ I delivered "insights" that no one could use.
❌ I was frustrated because nobody "got it."
➡️ 𝗗𝗮𝘁𝗮 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗶𝘀 𝗷𝘂𝘀𝘁 𝗿𝗮𝗻𝗱𝗼𝗺 𝗻𝗼𝗶𝘀𝗲

𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝟯 𝘁𝗵𝗶𝗻𝗴𝘀 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼 𝘁𝗼 𝗽𝗿𝗲𝘃𝗲𝗻𝘁 𝘁𝗵𝗶𝘀:

𝟭. 𝗧𝗮𝗹𝗸 𝘁𝗼 𝗽𝗲𝗼𝗽𝗹𝗲
Don't guess what they need - have a conversation!

𝟮. (𝗧𝗿𝘆 𝘁𝗼) 𝘀𝗽𝗲𝗮𝗸 𝘁𝗵𝗲𝗶𝗿 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲
Stop hiding behind your tech jargon and try to understand their goals and challenges, even if this takes 2 or 3 calls

𝟯. 𝗦𝘁𝗮𝗿𝘁 𝘀𝗶𝗺𝗽𝗹𝗲
Take 1 problem and solve it with the minimum setup
➡️ Then iterate and build upon this


𝗬𝗼𝘂 𝗮𝗹𝘀𝗼 𝘄𝗼𝘂𝗹𝗱𝗻'𝘁 𝗰𝗼𝗼𝗸 𝘂𝗽 𝗮 𝟱-𝘀𝘁𝗮𝗿 𝘀𝘁𝗲𝗮𝗸 𝗱𝗶𝗻𝗻𝗲𝗿 𝗶𝗳 𝘆𝗼𝘂𝗿 𝗴𝘂𝗲𝘀𝘁𝘀 𝗮𝗿𝗲 𝘃𝗲𝗴𝗲𝘁𝗮𝗿𝗶𝗮𝗻𝘀 😜

---

Hi, I'm Fabian👋
I write about actionable data systems on the GCP
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Don't rely on goals for the new year.
𝗕𝘂𝗶𝗹𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 ⤵️

We all know the drill.
1st of January.
Motivation is high.

You set giant, ambitious goals.
❌ "I'll read 50 books."
❌ "I'll finally get fit and live more healthy."
❌ "I'll become an expert in Python, AWS and Snowflake."

By February, life happens.
Motivation fades.
You quit.

I used to fall into this trap every single year.
Until I shifted my mindset from 𝗚𝗼𝗮𝗹𝘀 to 𝗦𝘆𝘀𝘁𝗲𝗺𝘀:

✅ "Read 50 books" ➡️ 𝗥𝗲𝗮𝗱 𝟭𝟬 𝗺𝗶𝗻𝘂𝘁𝗲𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝗯𝗲𝗱. 
✅ "Get fit " ➡️ 𝗧𝗿𝘆 𝘁𝗼 𝗴𝗲𝘁 𝟭𝟬𝗸 𝘀𝘁𝗲𝗽𝘀 𝗶𝗻 𝗲𝗮𝗰𝗵 𝗱𝗮𝘆. 
✅ "Master Python" ➡️ 𝗟𝗲𝗮𝗿𝗻 𝗳𝗼𝗿 𝟭𝟱 𝗺𝗶𝗻𝘂𝘁𝗲𝘀 𝗮 𝗱𝗮𝘆.

This removes the pressure and focuses on consistency.
And remember my favorite equation: 𝟭 > 𝟬
Doing a tiny bit is always better than doing nothing at all!


Here's how I approach 2026:
• 10min of self-compassion and gratitude before bed
• 20min of working and coding on a new pet project 
• Cold shower immediately after waking up
  (let's see if this one will work out 😅)


𝗣𝗦: In 2026 I will also start the biggest thing in my professional career, but more on that tomorrow 😉 

---

Hi, I'm Fabian👋
I write about actionable data systems on the GCP
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Anthony Alcaraz

Writes on ML/LLM Ops 🎨 | Follow me (4,8 K+) 🔔

I help AI startups go from promising to unstoppable.

I also write about it, my book Agentic Graph RAG (O'Reilly, 2026) is the practical blueprint for building AI systems that combine retrieval, reasoning, and autonomous decision-making at enterprise scale.

https://www.oreilly.com/library/view/agentic-graph-rag/9798341623163/

My career has been a journey across both sides of the table: building AI products as a CTO, transforming legacy systems as a Field CTO for enterprises and government, and now at AWS, helping the most exciting GenAI startups scale across Europe and the US.

I've shipped GenAI platforms, deployed MLOps pipelines serving millions, advised L'Oréal on AI-for-BI transformation, and lectured at Oxford on the intersection of LLMs and graphs. But what gets me out of bed is working with founders who are solving hard problems with bold ideas.

At AWS, I connect high-potential startups with the resources, partnerships, and enterprise pathways they need to grow. Before that, I was in the trenches, building, breaking, and shipping.

If you're a founder, VC, or enterprise leader thinking about GenAI, agentic systems, or knowledge graphs, I'd love to chat.

Chief AI Officer & Decision scientist

Management in AI Projects and Engineering Teams:
In my managerial role, I oversee AI projects and lead teams of engineers in crafting cutting-edge solutions. My focus is on driving strategic initiatives, ensuring projects are aligned with organizational goals, and fostering a culture of innovation and excellence. I take a hands-on approach to problem-solving and mentorship, ensuring that both projects and people reach their fullest potential.

Development in LLM/ML Ops:
My development expertise lies in the specialized domains of Large Language Models Operations (LLM Ops) and Machine Learning Operations (ML Ops). I am responsible for streamlining machine learning workflows from development to deployment. My work enables organizations to not only adopt but excel in leveraging machine learning technologies, facilitating continuous learning and adaptability across the board.

Marketing and Strategic Partnerships:
In the realm of marketing, I focus on building and nurturing strategic partnerships. My efforts are geared towards creating synergies with like-minded organizations and industry leaders. By forming valuable partnerships, I amplify brand visibility, foster innovation, and open doors to new business opportunities. The result is a win-win situation for all stakeholders involved, enhancing both market reach and impact.

The strongest multi-agent architecture has one rule:
No agent calls another agent. Ever. 💢 

Most agent systems work like phone calls. The orchestrator dials Agent A, waits, dials Agent B with the result, waits again. 

If B hangs up, everyone downstream sits in silence.

Queue-based architecture works like a mailroom. Agents subscribe to event streams. They listen for messages matching their capability. They process, drop output into the next queue. No agent knows another agent exists. The queue is the only shared interface.

Agent A publishes to topic:tasks.parsed.
Agent B subscribes to topic:tasks.parsed.
Agent B publishes to topic:tasks.enriched.
Agent C subscribes to topic:tasks.enriched.

No function calls between them. No shared state. No waiting.

A meta-orchestrator reads every topic. It holds the full pipeline schema. When Agent B crashes, messages pile up in tasks.parsed with no consumer.

It restarts B, scales a second instance, or reroutes. Other agents never notice.

Four layers make this work:

Layer 0 — STATE: External stores (database, graph). Events carry references, not payloads.
Layer 1 — EVENT BACKBONE: Kafka or Redis Streams. Durable, ordered, replayable event log.
Layer 2 — AGENTS: Stateless consumers. One capability each. Subscribe input, publish output. Errors to dead letter queue.
Layer 3 — META-ORCHESTRATOR: Reads ALL topics. Tracks events via parentId chains. Detects stuck agents, poison pills, throughput drops.

Confluent documents four patterns on this backbone: Orchestrator-Worker, Hierarchical, Blackboard, Market-Based. Kai Waehner pairs Kafka with Flink for stateful stream processing. Anthropic's MCP and Google's A2A route through the event broker, replacing point-to-point APIs.

The agent contract is minimal: subscribe to T_in, consume event with id/type/payload/metadata, transform, publish to T_out with parentId linking back. On failure, publish to T_dlq with error context.

Every agent becomes a 200-400 line module. Independently deployable. Independently testable. Replaceable without touching anything else.

Ali Pourshahid at Solace calls this the "agentic AI mesh." Alan Nichol at Rasa warns the alternative is a distributed monolith — agents calling each other are monoliths with extra network hops.

The queue turns coordination into infrastructure. Agents stop calling. They start listening.

Scaling Agentic Startups to Enterprise @AWS | Author of Agentic Graph RAG (O’Reilly) | Business Angel

Your systems of record are lying to your AI agents. 🎶 

Ask sales for ARR: one number. 
Ask finance: different number. 
Ask accounting: something else entirely.

Now tell an agent to "calculate ARR for the board deck."

Which number does it use? When systems drift by 3%, which one wins?

The fragility isn't your model. 

It's whether the agent pulled the right value from the right system at the right time.

🔴 The Core Problem

Your warehouse stores state. It doesn't store reasoning, the logic that connects state to action.
Semantic layers define "official_arr." They don't capture that for board decks you exclude trial ARR, or that finance wins when sales disagrees.
That knowledge lives in tribal memory. And agents can't access tribal memory.

🟡 What Agents Actually Need

Three capabilities your stack doesn't have:
Full recursion, traverse ownership chains of arbitrary depth. 
Ontological inference, derive facts not explicitly stored. PTIME complexity, guaranteed reasoning termination.

SQL can't do this. Standard graph databases can't either.

🟢 The Transition That Works

Most enterprises assume agentic AI requires data migration.

It doesn't.

One bank tried the migration path: 4 people, 1.5 years, still couldn't explain credit decisions.

Same bank with a reasoning layer over existing data: delivered in weeks. 17-minute decisions became 5 seconds.

Bring reasoning to your data. Not data to reasoning.

🔵 The Data Strategy That Emerges

You have two distinct problems requiring two distinct solutions.

1️⃣  Problem one: Your agents need real-time memory. Conversation state. Session context. The ability to blend graph traversal with vector similarity in a single atomic transaction. Data that changes millisecond to millisecond.

This is an operational data problem. SurrealDB solves it. One engine handling graph, document, vector, relational, and time-series with unified ACID guarantees. Your agent's working memory lives here. LIVE SELECT pushes changes instantly. No eventual consistency bugs between separate stores.

2️⃣  Problem two: Your agents need to reason over enterprise knowledge. Derive conclusions from facts spread across Snowflake, Databricks, Postgres, and Oracle. Provide proof trees auditors can verify. Handle recursive ownership chains of unknown depth. Infer what must exist even when data is missing.

This is a reasoning problem, not a storage problem. Prometheux solves it. Connects to your existing data infrastructure without migration. Executes Warded Datalog+/- with mathematical guarantees no warehouse can provide. When your compliance agent needs to explain why a credit decision was made, the proof tree traces from conclusion back to source facts automatically.

These layers don't compete. 

They compose.

Your warehouse remains the analytical source of truth. 
Your operational systems remain the transactional front door.

Scaling AI Startups to Enterprise @AWS | Author of Agentic Graph RAG (O’Reilly) | Business Angel

Context graphs exploded in January 2026. 🤯 

I synthesized 6 leading perspectives into one framework.

Here's what the smartest people in enterprise AI are actually saying:

1. Arvind Jain (Glean CEO) - "Capture the How, Not the Why"

Systems capture final states. But work happens in Slack, docs, and meetings.

The "why" is unobservable. The "how" leaves a digital trail.

Over many cycles, process traces approximate the why.

2. Jaya Gupta (Foundation Capital) - "Security Is Existential"

Decision traces are the moat, but only if you make them safe.

Four primitives needed:
→ Stable pseudonymization
→ Synthesis-level conflict checks
→ Real-time policy enforcement
→ Sanitization before indexing

"Agents collapse the security boundary that human judgment provides."

3. Anshul Gupta (Actively AI) - "Salesforce Has Lost the Deal"

Salesforce is a system of record, not a source of truth.

"Ask any sales leader how they get up to speed on a deal. Salesforce is the last place they'd look."

The right unit isn't the opportunity. It's the account.

Per-account agents compound. Workflow agents don't.

4. Justin Johnson (Run Data Run) - "The Hard Part Is the Clock"

Both ontology camps argue about nouns.

Meanwhile, agents fail because they can't tell time.

"Your CRM knows Sarah is a VP. It doesn't know she was a Director until June—which explains why Finance flagged the pricing exception."

Temporal validity is the missing layer.

5. Diego Lomanto (WRITER) - "Enterprise Brain Must Be Stateful"

Most enterprise AI is stateless, each query starts fresh.

Enterprise brains remember, connect, and compound.

"Being adjacent doesn't compound. To capture reasoning, you need to be there when the decision commits."

Marketing is the proving ground: high volume, inherent ambiguity, unique executions.

6. Dharmesh Shah (HubSpot) - "The Three-Car Garage Problem"

"Building context graphs when you haven't deployed agents at scale is like installing a three-car garage when you don't own a car."

Most companies still:
→ Struggling with basic data unification
→ Figuring out if AI can handle tier-1 support
→ Haven't deployed agents at scale

Context graphs are inevitable. The timing is the question.

Six critical gaps remain:

1. Governance: No confidence decay or contradiction detection
2.*Security: Decision traces harder to anonymize than documents
3. Continuity: Workflow agents don't compound
4. Temporality: Systems can't answer "what was true when?"
5. Statefulness: Most AI is stateless
6. Adoption: Prerequisites aren't met for most companies

The architectural truth:

Layer 7: Context Graphs (memory)
Layer 6: Individual Brain (assistants)
Layer 5: Skills (portability)
Layer 4: Tool Orchestration (typed graphs)
Layer 3: Context Layer (access)
Layer 2: Control Layer (correctness)
Layer 1: Structured Output (composability)
Layer 0: Agent Harness (reliability)

If any layer = 0, total value = 0.

Context graphs are Layer 7. Build from the bottom up.

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