Aleksej Bulygin

Data Architecture Lead / CDO — Marketing & Advertising Data Platform

• Project Goal: Designed and delivered a unified platform for collecting, aggregating, and visualizing marketing and advertising data from dozens of disparate sources.
• Architecture & Technology Leadership:
  Selected optimal technologies and cloud solutions to meet business and technical requirements.
  Designed cloud-based DWH architecture for data ingestion, storage, processing, and visualization.
  Developed API integrations for seamless data exchange between systems.
  Built a service layer between backend systems and Google Cloud Platform components.
• Hands-on Development:
  Personally implemented data integrations, event pipelines, and processing services.
  Configured and optimized dataflows for large-scale ETL and streaming workloads.
• Tech Stack:
  GCP: Cloud Composer, Cloud Functions, Pub/Sub, BigQuery, Cloud Storage, Secret Manager, Dataflow
  Languages & Tools: Python, Git, Ruby

Key Achievements

• Delivered a scalable, cloud-native data platform consolidating dozens of marketing and advertising data sources into a single analytics tool.
• Improved marketing decision-making by providing real-time and historical performance insights in one system.
• Balanced leadership and architecture responsibilities with direct contribution to core development, ensuring both technical excellence and timely delivery.

Head of Data & Analytics / Data Science Lead — Team Management, Data Infrastructure, Machine Learning

• Team Leadership & Management
  Led a cross-functional team of data analysts, data scientists, and ML engineers.
  Defined the technology stack and analytical tools for multiple projects.
  Conducted backlog grooming, prioritization, and continuous progress monitoring.
  Collected business requirements and conducted customer development interviews with internal stakeholders.
  Provided hands-on technical mentorship and practical problem-solving guidance to the team.
• Data Infrastructure & Architecture
  Designed and implemented an event tracking system based on RudderStack, S3, and Greenplum.
  Built analytical data marts with pre-calculated metrics for visualization in Tableau.
  Architected and developed a cross-user identification and profiling system using Dedupe, Splink, and Spark.
  Designed DWH architecture following the Data Vault methodology.
  Improved collaboration processes between analytics and engineering teams.
• Machine Learning & Analytics Solutions
  Developed an unsupervised learning model for suspicious traffic detection.
  Implemented multi-product analytics systems for Dosmart, Aitube, AituPassport, AituMessenger, and Ainews Metrics.
• Reporting & Stakeholder Communication
  Created Tableau dashboards and ReDash reports for KPI monitoring.
  Regularly reported project statuses to leadership and stakeholders.
  Ensured team well-being through regular performance monitoring and feedback sessions.

Tech Stack: Python, Kafka, Greenplum, Tableau, Airflow, DBT, ReDash, GA, PostgreSQL, ClickHouse, Amplitude, GTM

Key Achievements

• Delivered scalable, event-driven analytics infrastructure across multiple high-load products.
• Implemented a cross-product user identification system, enabling unified user profiling and improved personalization.
• Improved data pipeline efficiency and reliability through architectural optimization and process automation.
• Strengthened inter-team collaboration, reducing project delivery time and improving data quality.

Lead Data Analyst / Data Engineer — DWH, ETL/ELT, Marketing Analytics

• Data Warehouse Development: Participated in the design and implementation of a DWH for marketing data storage.
  Defined architectural solutions for database design.
  Gathered and documented business requirements in collaboration with stakeholders.
  Designed and optimized data models for analytical needs.
• Data Pipelines & Processing:
  Developed and maintained ETL and ELT pipelines for large-scale marketing data ingestion.
  Refactored dozens of Apache Airflow DAGs, improving performance, maintainability, and error handling.
• Team Leadership:
  Managed a team of four analysts, providing technical guidance, mentoring, and task prioritization.
• Analytics & Reporting:
  Developed a marketing reporting system, enabling stakeholders to track campaign effectiveness and ROI.
  Built annual KPI forecasting models based on historical data, supporting strategic planning.
  Designed and implemented a multi-channel attribution model, improving budget allocation efficiency across marketing channels.

Key Achievements

• Successfully delivered a scalable DWH solution that reduced data retrieval time and improved reporting accuracy.
• Streamlined ETL processes, leading to faster data processing and fewer operational errors.
• Improved marketing ROI through advanced attribution modeling and data-driven planning.
• Enhanced team performance through leadership, process optimization, and knowledge sharing.

Machine Learning Engineer / Data Scientist — Scoring, Fraud Detection, DSS & DMS Systems

• Developed machine learning models for credit scoring, decision-making systems (DSS), document management systems (DMS), and fraud monitoring.
• Specialized in real-time customer scoring and lead ranking, enabling instant decision-making and improving conversion rates.
• Designed and deployed ranking algorithms for both batch and real-time processing pipelines.
• Extensive hands-on experience with PySpark for distributed data processing and NoSQL databases for high-throughput applications.

Stakeholder & Communication Achievements

• Conducted stakeholder interviews to translate business needs into actionable data science tasks.
• Delivered multiple high-impact presentations on project results and department achievements to management and cross-functional teams.

Process & Culture Improvements

• Introduced code cross-review practices, enhancing team-wide coding standards and facilitating skill sharing.
• Implemented development vs. production environment separation, significantly reducing release errors.
• Established Git-flow branching strategy, improving release stability and collaboration efficiency.

Key Achievements

• Designed real-time scoring models that reduced decision latency and improved accuracy in lead prioritization.
• Strengthened development culture, resulting in fewer production incidents and faster onboarding for new engineers.
• Delivered analytical and ML solutions directly aligned with business KPIs through close collaboration with stakeholders.

Life.ru — Product & Data Analytics

• Increased website traffic by 20% through data-driven content recommendations.
• Developed custom editorial performance metrics, improving content quality and audience engagement.
• Conducted advanced product and data analytics to identify growth opportunities.

Bubble.ru — Product Analytics, Marketing Analytics, Data Migration

• Designed and implemented a data migration system from 1C-Bitrix to a modern tech stack (PostgreSQL, Ruby on Rails).
• Built marketing analytics dashboards, enabling the team to make informed campaign decisions and improve ROI.

Bloggy.top — Backend Development, Machine Learning

• Developed backend infrastructure and implemented ML models for content ranking and personalization.
• Created a ranking formula improving content discoverability and user retention.

Football Players Metrics Platform — Backend Development, Sports Analytics

• Designed and developed a platform for collecting, aggregating, evaluating, and visualizing data on amateur football clubs.
• Built web scrapers, aggregation pipelines, and metric calculation algorithms.
• Implemented backend architecture supporting scalable data processing.

Technical Skills

• Languages & Tools: Python, SQL, Pandas, NumPy, Scikit-learn, TensorFlow, Keras, Matplotlib, Seaborn
• Specialized Expertise: Time series analysis, natural language processing (NLP), statistical modeling, machine learning model development, data cleaning & pattern recognition
• Data Engineering: Data migration, ETL pipelines, backend API development

Key Achievements

• Delivered measurable business impact by boosting engagement metrics and traffic across multiple projects.
• Developed custom analytics solutions tailored to business needs, enabling data-driven decision-making.
• Designed and deployed machine learning models for ranking, prediction, and personalization tasks.
• Successfully migrated large-scale data systems to modern, scalable architectures.