Business Informatics · Data Engineering · Automation & Integration · Security-aware Data Systems
I am completing a BSc in Business Informatics at FH Technikum Wien and build reproducible data and automation systems with an emphasis on data quality, reliability, traceability, clear system boundaries, and governance.
My current technical direction is Data Engineering → Data/ML Platform Engineering, with security and governance as complementary engineering concerns rather than substitute claims for engineering experience.
Currently interested in internship, working-student and junior opportunities in Austria across Data Engineering, Data Platforms, Data Quality, Automation/Integration, and technical Data/AI Governance.
My strongest collection of implemented Data Engineering work.
Euro Air Quality Pipeline
- multi-source batch and streaming ingestion
- Python, PostgreSQL, Apache Kafka and Spark Structured Streaming
- Bronze / Silver / Gold architecture and Apache Parquet
- explicit schemas, deterministic event IDs, deduplication and Data Quality checks
- reproducible local infrastructure with Docker Compose
Weather Air Vienna adds a reproducible REST API → MongoDB → staging → daily aggregation pipeline.
CloudOps Insight Lake is an AWS-oriented data-platform project in active development. Its current repository evidence focuses on architecture, source contracts, data grain, identity, Data Quality, observability, IAM design and cost-aware decisions before cloud implementation.
My current cross-disciplinary flagship project: a proof of concept for recurring security-control evidence, follow-up and reporting processes.
- Microsoft Forms and Power Automate evidence-intake and reminder workflows
- deterministic Python ETL with 10 explicit Data Quality rules
- controlled operational snapshot → curated reporting boundary
- source-controlled Power BI PBIP / PBIR / TMDL project
- explicit semantic model and 21 version-controlled DAX measures
- Management Overview and Control Monitoring report pages
- 53 automated tests and GitHub Actions CI
- minimized AI-review queue with final governance decisions kept human-controlled
The repository deliberately distinguishes evidence presence, compliance, timeliness, workflow state and Data Quality instead of collapsing them into one synthetic status.
Academic distributed-systems project demonstrating broader software and platform engineering fundamentals.
- six independently startable applications
- Java and Spring Boot
- RabbitMQ event-driven messaging
- PostgreSQL, Spring Data JPA and Flyway migrations
- read-only REST boundary and JavaFX client
- Docker Compose infrastructure
- explicit service ownership, message contracts and end-to-end verification documentation
This project is useful evidence for asynchronous processing, system boundaries, persistence ownership and integration design beyond Python-only data pipelines.
Two implemented automation/integration modules complement the data-engineering portfolio.
REST API Integration Hub
- FastAPI, PostgreSQL, Docker Compose and n8n
- REST APIs and webhooks
- HMAC verification, idempotency and replay protection
- structured audit logging and Dead Letter Queue handling
AI Workflow Automation Engine
- webhook-driven operational intake workflow
- structured LLM extraction with deterministic mock mode
- PostgreSQL persistence for workflow state, events, errors and metrics
- n8n branching with mock Jira / Slack targets
- KPI summary endpoint with explicitly illustrative automation-impact estimates
The integrations are simulated portfolio systems and are not presented as production deployments.
| Area | Current evidence |
|---|---|
| Data Engineering | Python, SQL, PostgreSQL, MongoDB, Kafka, Spark Structured Streaming, Parquet, batch/streaming pipelines |
| Data Quality & Modeling | explicit schemas, validation, deduplication, grain, identifiers, data contracts, curated reporting boundaries |
| Automation & Integration | REST APIs, FastAPI, webhooks, n8n, Microsoft Power Automate, RabbitMQ |
| Systems Engineering | Java, Spring Boot, Docker Compose, event-driven processing, service boundaries, persistence ownership |
| Reporting & Governance | Power BI, DAX, lineage, traceability, auditability, evidence-oriented workflows |
| Engineering Practice | Git/GitHub, pull-request workflows, automated tests, CI, architecture documentation, explicit limitations |
- AWS Data Engineering Floci Lab — local AWS-compatible hands-on environment with explicit separation between emulated behavior and real AWS capabilities.
- Agentic AI Projects — bounded AI prototypes focused on controlled tool use, structured outputs and security-aware design; one incident-analysis agent is currently implemented.
- Secure AI Fraud Detection Pipeline — synthetic-data ML engineering project with feature engineering, model evaluation, API scoring, threat modeling and governance documentation.
- Healthcare AI Governance Assessment — Streamlit-based educational governance prototype with role-specific views and basic audit logging.
My earlier professional experience spans education and technical live-production environments. That background contributes practical strengths in structured communication, requirements translation, documentation, handover, troubleshooting and reliability under time pressure.
I use GitHub as engineering evidence rather than as a course-completion log: the strongest repositories document what is implemented, how it is verified, what remains planned, and where the limits are.
Engineering principle: implemented capability, simulated integration, planned architecture and production experience are different evidence levels. I keep those boundaries explicit.