Foundations & Infrastructure
Thirteen years of it — from one-man IT department to a five-person team. The layer everything else quietly depends on.
- Windows Server
- Linux
- VMware
- Active Directory
- Cisco
- Azure / AWS
Mustafa Buğra Balaban, M.Sc. — IT Manager → Data Engineering
I kept the systems running.
Now I make the data flow.
Thirteen years of being the person who fixes everything — ERP, networks, factory floors, people's Mondays. That breadth is now pointed at one craft: building reliable data pipelines.
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01 — The Shift
Generalists don't start over.
They converge.
Years in IT operations teach you something no bootcamp can: how systems actually fail. Disks fill. Jobs hang. The backup that "always works" doesn't. You learn to think in dependencies, blast radius, and 3 a.m. rollbacks.
I've already lived this once. I architected a Manufacturing Execution System — grown out of my Systems Engineering master's thesis — that became the backbone of an international fashion manufacturer: 800+ clients, 700+ data terminals, and factory reporting cut from one day to real-time. That was data engineering before I knew to call it that.
So this isn't a career change. It's compression: everything learned keeping infrastructure honest, applied to keeping data honest.
# same instincts, new target
$ career --migrate it_manager data_eng
→ carrying over: t-sql, erp, mes_at_scale
→ carrying over: 99.9%_uptime, incident_calm
→ carrying over: team_building, translating_tech
$ airflow dags trigger trust_pipeline
✓ 14 tasks succeeded, 0 silent failures
$ uptime --career
"since 2010, still curious"
800+MES clients served
700+data terminals
99.9%infrastructure uptime
1d → livereporting latency
02 — What I Do
Three layers, one through-line: reliability.
Pipelines & Movement
The new center of gravity. Extract, load, transform — scheduled, idempotent, observable, and boring in the best way.
- Python
- pandas
- Spark
- Airflow
- ETL / ELT
- Docker
Warehouses & Trust
Where data becomes decisions. Modeling, query optimization, and quality checks so the numbers mean what people think they mean.
- MSSQL
- PostgreSQL
- MySQL
- Data modeling
- Query optimization
- Real-time reporting
03 — How I Work
Principles forged where downtime stops a factory.
№ 01
Boring is a feature
The best pipeline is the one nobody talks about. Predictable beats clever; idempotent beats fast. Excitement belongs in the results, not the infrastructure.
№ 02
Trust the logs, not the vibes
Every claim about a system should be checkable. Monitoring, data tests, and lineage aren't overhead — they're the difference between knowing and hoping.
№ 03
Breadth is leverage
Knowing the network, the OS, the database, the ERP, and the factory floor means debugging the whole stack — not just the layer in the job title.
№ 04
Translate, always
Years of explaining outages to non-technical people is exactly the skill data work needs: turning rows and DAGs into decisions humans can act on.
04 — Contact
Always happy to talk data.
Based in Lüneburg, Germany — fluent in Turkish, English, and steadily better German. The best place to find me is LinkedIn. I bring the calm of someone who has restored production at 3 a.m. — and the curiosity of someone who chose to learn this on purpose.