01 / Data products · Platforms · Analytics
I’m Turker, a hands-on Head of Data & AI with 10+ years of experience across B2B SaaS, fintech, and regulated environments. I build scalable data platforms, lead high-performing teams, and turn analytics and AI into products that support clearer decisions and measurable growth.
02 / Open source
Selected upstream contributions across collaborative software, analytics, and data integrations. Each item links to the project and the original pull request.
A multiplayer canvas for orchestrating coding agents. My contributions span executor reliability, MCP and OAuth integrations, and board interaction design.
An open-source data exploration and visualization platform. I contributed fixes and documentation around datetime correctness and the project showcase.
An open-source data integration platform. My contributions to the HubSpot source connector span custom association handling and an experimental stream for full workflow definitions from the Automation v4 API.
03 / Published notes
Field notes on building dependable software and working with intelligent systems.
As collecting and querying product events gets cheaper, the value moves to metric definitions, behavioral correctness, and the loop around the data.
Read articleAttention on X and enterprise adoption tell different stories: why n8n’s business kept growing as the conversation moved to coding agents.
Read articleA trip to Issyk-Kul’s Cholpon-Ata turned into an etymology lesson: the Central Asian Turkic legend behind the word “Ata,” and why the “Shepherd’s Star” has nothing to do with shepherds.
Issık-Göl kıyısındaki Cholpon-Ata gezisi bir etimoloji dersine dönüştü: “Ata” kelimesinin arkasındaki Orta Asya Türk efsanesi ve “Çoban Yıldızı”nın aslında çobanlarla ilgisi olmadığı gerçeği.
Read articleI have also been responsible for compliance processes at the company where I have worked for about four years. I started this series because of the questions I have received about the subject recently.
Read articleAs agents take on longer, harder work, isolated worktrees, durable context, visible dependencies, and human review become essential infrastructure—not optional process.
Read on XI think the hardest part of using AI agents in a data organization is not the model, it is the context. A data team’s work spans very different domains: ingestion, orchestration, reporting, BI, CRM data. An agent that is pointed at all of it at once has to guess which part matters, and it often guesses wrong.
We handle this with Agor, a shared canvas for running AI coding agents. People and agents work on the same boards instead of in separate terminals, and each task runs on its own isolated git branch. The unit of organization is the board, and we keep one board per domain: data ingestion and connector operations, pipeline orchestration and DAG development, analytics and reporting, the BI platform, CRM data flows, the exploration of new open-source tools, and the recurring team updates.
A board collects the branches, sessions, and notes of its own domain. When an agent starts a task on the ingestion board, it works inside ingestion context, not inside everything the team has ever done. The separation is simple, and I think it is where most of the output quality comes from.
04 / About
Over the past ten years, I have worked across fintech, digital banking, and B2B SaaS, building data platforms, decision systems, and data products. I see data as more than a reporting function: it should be a dependable capability that supports product development, growth, profitability, risk management, and everyday decision-making.
My experience covers the full lifecycle, from strategy and governance to analytics, machine learning, and production delivery. I have built and led multidisciplinary teams, developed products used by both internal teams and external partners, and worked within demanding privacy, security, and regulatory requirements.
I enjoy working where data, technology, and business needs meet—helping teams establish clear foundations, ask better questions, and turn useful ideas into systems that can be trusted over time.
MSc in Finance
2018 · GPA 3.87 / 4.00
BSc in Management · Minor in Economics
2015 · GPA 3.36 / 4.00