I’m a Data Engineer with 6+ years of professional experience, with Ab Initio at the core of my engineering background.
My experience spans enterprise-scale data integration, high-volume ETL systems, production data pipelines, data quality, automation, performance tuning, and operational support.
Over the years, my interests have expanded beyond traditional data engineering into distributed computing, systems programming, data science, AI infrastructure, and high-performance computing.
I enjoy understanding not only how a system works, but also why it behaves the way it does under real workloads.
- 💼 6+ years of professional experience in Data Engineering
- 🔷 Strong professional background in Ab Initio
- 🏗️ Experience with enterprise-scale ETL and data integration systems
- 🎓 Graduate background in Data Science
- ⚙️ Interested in distributed and performance-oriented systems
- 🧠 Exploring AI, local inference, and resource-efficient computing
- 🦀 Expanding deeper into Rust and systems programming
- 🇩🇪 Based in Germany
Ab Initio has been the foundation of my professional engineering career.
My experience includes working with areas such as:
GDE · Conduct>It · Control Center · Express>It · TRW
along with:
ETL / ELT · Data Integration · Data Quality · Batch Processing · Production Support · Performance Tuning
I have worked on data-processing environments where correctness, reliability, scalability, operational stability, and recoverability are critical.
My broader data-engineering experience also includes:
SQL · Oracle · Python · Shell / KornShell · PostgreSQL
My interests have gradually expanded from enterprise ETL into larger distributed and performance-oriented architectures.
Areas I work with or actively explore include:
Kafka · Airflow · Ray · AsyncIO · Parallel Processing · Distributed Computing
I’m particularly interested in how large workloads can be processed efficiently across different execution models.
I enjoy going below high-level abstractions when performance or system behaviour requires it.
My current areas of exploration include:
Rust · C/C++ · Concurrency · Memory · Runtime Architecture · ABI · Performance Engineering
I’m interested in understanding how software interacts with operating systems, runtimes, memory, and hardware rather than treating those layers as black boxes.
My graduate studies expanded my engineering background into Data Science and machine learning systems.
Areas I’m interested in include:
Machine Learning · Local AI · LLM Inference · Data Processing · Resource-Constrained AI
I’m particularly interested in making AI workloads more efficient and understanding what happens when compute, memory, or hardware resources become the limiting factor.
More recently, I’ve been exploring computing closer to the hardware.
Areas of interest include:
GPU Computing · CUDA · Metal · Apple Silicon · Parallel Computing · Hardware-Aware Optimization
This is a natural extension of the same engineering mindset I developed through data engineering:
understand the data flow, understand the execution model, identify the bottleneck, and improve the system.
I tend to approach engineering problems by asking:
- Where is the real bottleneck?
- How is data moving through the system?
- What happens when the workload grows?
- Can execution be parallelized safely?
- What happens when a dependency fails?
- Is the abstraction helping or hiding the actual problem?
- Can the system be made simpler, faster, or more reliable?
I enjoy moving between high-level data architecture and low-level system behaviour.
Alongside professional engineering, I enjoy experimenting with areas outside my immediate day-to-day work.
My broader interests include:
- distributed data processing
- systems programming
- GPU computing
- local and resource-efficient AI
- runtime behaviour
- hardware-aware software
- performance experimentation
- research-oriented data pipelines
I also have an academic research background, including earlier work involving indoor navigation and location-aware systems.
Data Engineering
Ab Initio · ETL / ELT · SQL · Oracle · PostgreSQL · Data Quality
Programming
Python · Rust · C/C++ · Shell · KornShell
Distributed Systems
Kafka · Airflow · Ray · AsyncIO · Parallel Processing
Systems & Compute
CUDA · Metal · Apple Silicon · Linux · macOS
Engineering Interests
Distributed Systems · Performance Engineering · Runtime Systems · AI Infrastructure · High-Performance Computing
My professional foundation remains Ab Initio and Data Engineering.
At the same time, I’m continuing to expand into areas that sit closer to computation and system internals:
Data Engineering → Distributed Systems → Performance Engineering → Systems & Compute
I’m particularly interested in engineering problems that cross the boundaries between data, software architecture, runtimes, and hardware.
I’m always interested in conversations around:
Ab Initio · Data Engineering · Distributed Systems · Performance Engineering · Systems Programming · AI Infrastructure · GPU Computing