Senior Data Architect and Machine Learning expert

I build data systems that move revenue.

Sixteen years of turning raw data into business outcomes: petabyte platforms, real-time ML, and the teams that run them.

Write me Download CV

TOP 3% TALENT

Vetted by Hire me

0+: years designing and running data systems
$0M+: of revenue protected by fraud detection and personalization
0 PB: of production data on one platform, built from zero
0K RPS: peak requests per second handled in real time

About me

I am a data executive specializing in data platforms, MLOps, and architecture, with a track record of building high-performance teams and systems that drive up to $0.5B in revenue. I work across strategy and execution: managing the full ML lifecycle, designing resilient architectures, and turning data into decisions that improve business outcomes.

Throughout my career I have led distributed teams, aligning engineering and business goals for maximum impact. I stay hands-on when it matters and follow the frontier closely, especially where large language models give companies a real competitive edge.

Role: Senior Data Architect, Head of Data, CTO
Focus: Data platforms, MLOps, real-time ML, LLM applications
Cloud: AWS and Google Cloud certified, Snowflake
Languages: English, Russian
Open to: Advisory, architecture reviews, hard ML problems

What I do

Six areas where I have shipped results at scale, not just slides.

Enterprise data management
Lakehouse platforms on Iceberg and Spark, governance, and cost control at petabyte scale.
Machine learning and AI
Recommendation, fraud detection, anomaly detection, and LLM products in production.
Data and software architecture
High-load systems, real-time pipelines, and multi-tenant SaaS designed for growth.
Team leadership
Building and leading distributed teams of 30+ engineers across backend, ML, and DevOps.
Cloud computing
AWS and Google Cloud architectures, migrations, and bills cut by a factor of three.
Strategic planning
Data strategy that ties engineering roadmaps to revenue and product goals.

Selected work

Each project starts with the number it changed.

$25M+ added monthly revenue

TangoMe

Real-time revenue engine

Built a production recommendation system and led a team of 30+ ML engineers delivering real-time personalization across 10K+ concurrent video streams, combining MLOps practices with low-latency architecture.
2 PB at 300K requests per second

Omniverse

Enterprise data platform from zero

Architected and scaled the data infrastructure from nothing to two petabytes. Implemented a Lakehouse on Apache Iceberg and Spark, launched near real-time BI, and deployed fraud detection that cut costs by 20% while reducing the Snowflake bill from $30K to $10K a month.
3 months from concept to production

Compliance SaaS, as CTO

Multi-tenant compliance platform

Led architecture and delivery of an enterprise compliance platform in SaaS and on-prem editions. Built backend, frontend, and DevOps teams, implemented a security-first design with Keycloak, and launched both the SaaS pilot and the on-prem package.
80% fewer false positives

AdTech and social platforms

AI-powered fraud detection

Designed and deployed anti-fraud systems that cut false positive rates by more than 80% and operational costs by 20%, combining machine learning with real-time processing to protect $500M+ in revenue.
Flagship anomaly detection product, MLOps built from scratch

Netwrix

Flagship UEBA product

Implemented the MLOps infrastructure from scratch and developed the anomaly detection system that became the company's flagship User and Entity Behavior Analytics product. Automated Spark test environments and set data management practices adopted company-wide.