About
Systems & Data Engineer with a foundation in Applied Mathematics. Over seven years, I have built end-to-end data ecosystems and cloud infrastructure — from raw data ingestion, cleaning, and transformation pipelines to distributed data lakes and GPU-accelerated clusters.
My work spans the full engineering lifecycle: designing high-throughput data pipelines, optimizing data structures, and establishing reliable DevOps and observability stacks (Kubernetes, Prometheus, Grafana, Loki). I focus on turning complex system dynamics into quiet, low-maintenance infrastructure, working best in lean teams that value technical clarity and strong documentation.
Feel free to reach out. I respond within a day with a clear technical perspective or next steps.
Engagement
Where I can be useful
Clear expectations from day one.
I partner with engineering teams and technical founders when systems require core architectural decisions, data pipeline optimizations, or reliability overhauls.
Designing scalable data and infrastructure foundations from scratch, optimizing high-throughput pipelines, untangling legacy monoliths, and building complete observability into cloud systems.
I deliver the highest value on core architecture, major upgrades, and complex data bottlenecks, rather than routine maintenance roles or projects without clear technical direction.
My Library
The deskbooks get reopened most months. The rest changed how I work once and stayed changed.
Designing Data-Intensive Applications
by Martin Kleppmann
The definitive guide on reliability, scalability, and structural integrity of data systems.
The Physics of the Future
by Michio Kaku
Exploring how physics will shape the world in the 21st century, from teleportation to space colonization. A mind-expanding journey through the potential of science.
Scale
by Geoffrey scale
Excellent overview of universal laws, applies to everything from biology to technology.
How to Solve It
by George Polya
A classic on problem solving and heuristics.
The Law
by Frederic Bastiat
Classic analytical thinking on system incentives, economic trade-offs, and unintended consequences.
The Road to Serfdom
by Friedrich Hayek
A foundational perspective on distributed information, market dynamics, and structural complexity.
Featured Work
Pipeline Acceleration, Queue Architecture & System Observability
Meteorological data analysis (SODAR, met-masts) was bottlenecked by monolithic Airflow DAGs, unorganized Celery queues, and fragile date parsing across disparate field data formats on GPU-accelerated Kubernetes.
Modularized Airflow on GPU Kubernetes and re-architected the Celery worker engine — partitioning soft vs. heavy workloads into dedicated Redis queues and API endpoints with parallel execution. Engineered a deterministic datetime parsing module to eliminate null gaps from non-standard field formats, and deployed the Grafana stack (Grafana, Loki, Prometheus) to capture empirical telemetry baselines before and after migration.
5–10xthroughput acceleration and zero queue starvation. Replaced guesswork with verifiable pre/post-migration telemetry snapshots, ensuring 100% time-series data continuity across all incoming field formats.
Career Timeline
- GE VernovaAug 2024 – Dec 2025System & Data EngineerBarcelona, Spain (On-site)
Maintained PyWRA — GE's internal scientific computing library — while refactoring Airflow on GPU Kubernetes and optimizing Celery queues in Redis. Led migrations to Python 3.11 and pandas 2, fixed critical wind rose rendering bugs, and built deterministic datetime parsers for field data.
5–10xDAG accelerationPythonNumPypandasPlotly+7Read case study - Trialing Health S.LDec 2023 – Aug 2024Data EngineerBarcelona, Spain (Onsite)
Built an AWS S3 Data Lake and Airflow ETL pipelines for medical research data across Iberian hospitals. Designed a Pydantic-based quarantine system for messy records, cutting data-to-insight latency from 120 to 40 minutes.
67%latency reductionAWS S3Apache AirflowPythonPydantic+2Read case study - Sembly AIJul 2021 – Aug 2023Backend & MLOps EngineerRemote
Started in backend engineering building Django APIs, internal dashboards, and MS Teams meeting bot integrations before transitioning to MLOps — modularizing ML monoliths with MLflow and introducing DVC for dataset/model versioning.
MLflow + DVCpipeline modularizationPythonDjangoDVCMLflow+4Read case study - Lemay.aiDec 2022 – May 2023DevOps & Systems EngineerOttawa, ON, Canada (Remote)
Managed bare-metal Proxmox hypervisors and AWS cloud infrastructure, deployed self-hosted GitLab CI/CD pipelines, implemented Kafka event streams, and built secure RBAC data ingestion layers.
Kafkaevent streamingGitLab CI/CDKafkaKafka StreamsProxmox+5Read case study - ElliscopeJul 2019 – Mar 2022R&D Engineer, Control & Energy SystemsOdesa, Ukraine (On-site)
Founded an R&D venture focused on autonomous aerial systems and hybrid energy solutions. Managed multidisciplinary teams and designed flight control systems.
5+ wksflight enduranceRustKalman FiltersMPCAerodynamics+2Read case study - Sembly AIOct 2019 – Jul 2021Backend Engineer (ML Infrastructure)New York, NY / Odesa, Ukraine
Established end-to-end automated training and validation workflows and architected a high-throughput audio ingestion system using Apache Kafka.
50%faster productionKafkaNLPFastAPIPython+2Read case study
Got a hard problem?
Tell me what is broken, what you are building, or what foundation needs laying. Rough notes are enough — I will read them properly and come back with something useful either way.