Case Studies

Several products and cross-functional teams need to operate as one system while business priorities, product backlogs, architecture, resources, and dependencies change at the same time.

Built the portfolio and cluster operating model: connected business needs to the roadmap and product and technical backlogs; aligned budgets, resources, timelines, risks, and cross-team dependencies.

Average time to market for product changes is 14 days. After the offer calculation and formation strategy was redesigned, the share of offers that ended in product signup increased by 30%. The estimated portfolio impact is several billion rubles.

The bank needed its own Decision Engine for real-time personalized offers, with production-grade reliability, scaling, and processing of tens of TB of data per day.

Took over the project as IT Lead in late September 2024 and formed the team by November. In December, already serving as Project Director, I started development and established the full engineering process: requirements, architecture, development, testing, CI/CD, releases, and operations.

The product entered production in April 2025, five months after development started. Its real-time and batch pipelines run on Apache Flink, Tarantool, and Kafka.

Specialists from the graph and geo platforms, NER services, and ML data labeling did not yet operate as one team. A shared process had to be established amid legacy migrations, technical debt, and fragmented CI/CD standards.

Brought the specialists together as one team and established the engineering process. Moved CI/CD toward one TeamCity pipeline, completed critical migrations, and built resource management. Initiated the technical expert community across T1 Group that became the Tech Guilds.

Automation up from 40% to 80% within the information system. Cut technical debt almost in half, and cleared the critical information-security and support issues.

The VTB graph platform needed to become the Mirion product, connecting data, ML/AI, and graph-analysis work with a coherent product architecture.

I prototyped ETL and ML/AI solutions, researched graph algorithms, and helped shape the product architecture. From May 2022, I led the analytics team and reviewed the main data flows.

The platform gained a product structure as Mirion. By February 2023, my scope had expanded to deputy IT Lead responsibilities and resource management.

Architecture practices lived in silos. Teams kept reinventing the same solutions across different products, technical debt kept growing, and there were no shared standards.

Initiated and launched the Tech Guilds community: recruited more than 20 senior experts for targeted assistance alongside wider peer support, secured support from the group leadership, and built shared standards and cross-team knowledge exchange.

By my estimate, community members resolved more than 1,000 engineering cases over the initiative’s lifetime and spread shared practices across teams. In T1 Digital Academy, I oversaw the Data Engineering course and restarted the systems analysis course, including its structure and learning materials.

The holding’s data architecture (reference and master data) was fragmented. Daily reporting to US investors in the parent group was breaking down.

Designed the data architecture and defended an adapted Data Governance project before top management — my first hands-on project management. Rebuilt the data flows on a waterfall model within the parent company. R&D on local ML models (pymorphy2, K-Means).

The solution scaled across the entire holding in Russia. The daily-reporting issues for US investors were resolved and operational risk dropped. Stack: Python, SQL, Pandas, NumPy, Seaborn, MS Visio, 1C (DO, KORP, ZUP, UPP, UT).

When several products are developed in parallel, process consistency, decision context, and quality controls are easy to lose, especially when AI agents participate in delivery.

Built a system that defines roles, stages, artifacts, and mandatory quality gates. LAF records decisions and helps move each product through one consistent engineering lifecycle.

I use LAF in my own projects and continue to develop it. Its backlog, isolated workspaces, automated checks, recovery mechanisms, and budget controls support background development. I am preparing it for external adoption.

Experience with real-time systems, data, and engineering management can help other teams when the context, constraints, and outcomes are explained.

Talks at HighLoad++ Saint 2024 ("Moving a Banking Product to Real Time"), Analyst Days 16 (2023, "Behold, Padawan: the Path of Data"), and Analyst Days 14 (2022, "Don’t Paint the Grass — Data Risks in a BANI World"). My own archive also includes the Analysis & PM Conference 2024 ("Accelerating Development with AI"). I also spoke at Impulse about data risks in a BANI world; I am still checking the year and exact title.

Built a sustained speaking record at major technology conferences. The HighLoad++ Saint 2024 talk is available on the HighLoad Channel on YouTube.

A leader needs to assess a large backlog quickly and understand who can do the work, in what order, and by when. Capacity, skills, and dependencies make manual planning cumbersome.

Built an assistant that accounts for team composition, capacity, dependencies, sprints, and the company’s configurable engineering process, then assigns work across specialists.

A working planning assistant with backlog structure, annual planning, dependencies, and capacity constraints. I develop it further and offer demonstrations and adaptation to company processes.