Albert Bikeev

- Led development of high-volume data features for the ad delivery service: cross-team collaboration, architecture, data modeling, testing strategy. - Led migration of complex data warehouse infrastructure from Hadoop stack to Snowflake, DBT and Airflow, optimizing scalability, maintainability, and query performance. - Optimization and cost reduction of deeply nested legacy data pipelines, translating business logic into modular, testable DBT models and orchestrating workflows using Airflow and Python.

- Designed and implemented large-scale data pipelines (up to few billions of data points) supporting real-time auctions and machine learning model relevance, in collaboration with data scientists. - Optimized and maintained the core ad delivery service responsible for serving hundreds of ad auction requests per minute, ensuring low-latency, high-availability and performance. - Primarily developed in Scala, with additional contributions in Python and JavaScript for internal tools and system integration.

Automated deployment, monitoring, and alerting for a metadata management system supporting the company’s Data Mesh initiative.

Worked in the team gathering and connecting data for the whole company and more. Which includes: - Building streaming ETL pipelines - Developing internal billing system - Learn how to gradually eliminate legacy and tech debt - Giving presentations to the team about Functional programming, concurrency and more. The company had serious troubles because of the Covid and almost everyone on probation period was laid off including me. I enjoyed the job and I believe that my team also liked working with me

Maintenance of distributed legacy system for fraud prevention: - Searching and fixing of bugs - Analyzing and fixing performance bottlenecks Development of some parts of the new fraud-prevention system: - Creating data-ingestion parts based on the legacy system implementation - Designing specifications for persistence component - Improving technical recruitment processes Buzzwords: Scala, Kafka, MongoDB, Elasticsearch

Development of a back-end for a platform of intelligent analytics and predictive analysis is a tool developed for industrial companies - Clover.global. In a small team of Scala developers, we built a Complex Event Processing engine for time-series data with DSL based interface for data-analysts and non-IT people who now can get a lot of insights about the data. Also, other teams in the company implemented analytical front-end on top of the engine (for visual monitoring and alerting).