Senior Data Engineer · 6+ years
I'm Oguzhan Ozturk. I design real-time pipelines, cloud-native architecture, and AI that earns its keep — for teams at Garanti BBVA, FREENOW, and Huawei.
Shipping data platforms at
A PostgreSQL car-listings ETL took 16 hours to backfill one large brand against a 4-hour SLA. The real problem wasn't the engine — it was running an OLAP workload on an OLTP store. A primer on the option landscape and a two-horizon path from quick win to platform.
Mirror a multi-tenant MySQL source into a queryable analytical store in under 5 minutes — built lean for a startup (~$200/mo) but designed to scale from 150M to 1B+ rows. CDC architecture, data modeling, failure handling, and the trade-offs.
Audit pipelines, modeling, and warehouse design — get a concrete, prioritized roadmap.
Kafka, Flink, CDC. Low-latency pipelines that hold up under real production load.
Legacy batch to cloud-native, Spark, and lakehouse — with a quantified business case.
LLM + vector search over your data, plus AI-augmented engineering practices that ship.
A map of the eleven open-source repos behind the way I work with coding agents now — grouped into the three things they actually fix: how the agent works, what it knows, and how much of the loop you have to crank yourself.
Most people still use coding agents like a chat box — prompt, wait, copy, fix, repeat. The next step is to stop prompting and design the loop that prompts the agent for you. Here's the shift, the repo that names it, and two tools that make a loop worth running.
A coding agent fails in two quiet, expensive ways: it forgets what it learned last session, and it re-reads your whole codebase to answer a small question. Four open-source tools attack both — memory, knowledge graphs, and context compression — and they map cleanly onto ideas any data engineer already trusts.
I help teams design real-time pipelines, cloud-native architecture, and AI that earns its keep.