Daniel Wolkow
Software Architect · Elixir/OTP · Data & Networks
I architect and build high-performance, fault-tolerant distributed systems.
With 8+ years of experience — from Linux kernel networking
to real-time traffic analysis at millions of events/sec.
I bridge the gap between low-level networking and
data-intensive pipelines.
Currently building my own search & analytics platform at D.Wolkow LLC.
About
I'm a software architect and engineer with a unique combination of expertise:
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Computer Networks — Linux kernel (C), BGP, DDoS mitigation, L2/L3 protocols
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Data Engineering & ML — real-time pipelines, time-series, anomaly detection, ClickHouse
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Functional & Systems Programming — Elixir/OTP, Python, Rust, Haskell
I design continuous data pipelines that process massive volumes with low latency,
and I'm passionate about building robust backend systems that scale.
Open to consulting, advisory roles, or innovative collaborations.
Languages I work with
Elixir / Erlang
Python
Rust
C
SQL
Bash
Preferred stack: Elixir/OTP for backend and distributed systems, Phoenix for web applications, Python/Nx for
data/ML, Rust for HPC
Experience
Founder & CTO
- Building a next-generation web search and analytics platform from scratch.
- Providing Elixir/OTP consulting — scalable backends, performance audits, and complex bug fixes.
Stack: Elixir, Phoenix, Erlang/OTP, ClickHouse
Research Engineer · AntiDDoS
- Architected a high-performance traffic analysis system in Elixir/OTP processing millions of
events/sec.
- Built BGP state management pipelines with guaranteed < 1 sec response to network
attacks.
- Developed ML services for traffic classification and user trust ranking.
- Implemented all internal web services for AntiDDoS system management.
Stack: Erlang/OTP, Elixir, Phoenix, ClickHouse, Redis, Kafka, Python, FastAPI, Docker
Data Scientist
- Time-series forecasting for the National Settlement Depository using LSTM-based models.
- Anomaly detection in trading data (MOEX) using information-theoretic approaches.
- Mass demand forecasting with smoothing models + residual modeling for speed and accuracy.
Stack: Python, ClickHouse, parallel computing
Linux Kernel Developer
- Optimized MPLS stack: 50–200% performance improvement by fixing data structure asymptotics.
- Implemented fast VLAN parameter changes without recreating child nodes.
- Added neighbour caching for ~15% throughput gain.
- Developed a DSA driver for Marvell switch (kernel 3.10, no upstream support).
Stack: C, gdb, perf, concurrency, algorithms
Education
Saint Petersburg State University
· Astronomy
2014 – 2019
Computer Science Center
· Software Engineering
2016 – 2018
Algorithms, Data Structures, C++, Parallel Programming, Programming Languages
Stepik
· Teaching & Course Author
active since 2015
Core Skills
Elixir / Erlang OTP
Python
ClickHouse
Real-Time Processing
Time-Series Analysis
Data Pipelines
Anomaly Detection
Linux Kernel
Distributed Systems
Network Programming
BGP / Routing
Phoenix / FastAPI
Docker / Kafka / Redis
Rust
Links