Vladyslav Vinnyk

Designed and implemented the Lakehousekeeper Platform for Databricks, managing TTL Retention and External Delta Tables Maintenance for all Lyft Offline Tables, processing 300K tables and 900M partitions, and removing 10PB of data.

Identified and resolved an Iceberg Maintenance DAG optimization, resulting in a $350,000 annual infrastructure cost reduction.

Engineered a custom cold data detection engine, delivering immediate $500,000 one-time storage savings and continuous annual reductions of at least $500,000.

Resolved a critical scalability bottleneck in a Legacy Offline Table Retention framework, increasing throughput by 20x–30x, ensuring a strict 10-hour SLA, and generating $415,000 in annual storage savings.

Led HMS to Glue data catalog migration efforts, implementing a user action ingestion pipeline for 5x faster table user detection and resolving metadata bottlenecks with 86x write and 100x read performance improvements.

Developed an end-to-end testing framework for table retention using Docker, reducing the feature-to-production feedback loop by 90% (from 15 days to under 2 days) and de-risking legacy system improvements.

Designed and deployed table-level cost attribution by scanning S3 objects with heuristics, providing precise compute and storage cost visibility across the entire Data Platform to aid user cost definition.

Conducted in-depth research on cutting-edge technologies, performing proof-of-concepts, documenting findings, and delivering presentations to inform strategic platform enhancements.

Designed and owned various critical system components, including orchestration, monitoring, table maintenance (compaction, snapshot cleaning), and data validations, leading design enhancement meetings.

Addressed intricate technical challenges such as memory leaks and performance optimizations, ensuring the stability and efficiency of the Data Platform.

Collaborated cross-functionally with various departments to gather requirements, collect feedback, and deliver tailored solutions and system optimizations.

Provided consistent Production On-Call support for one week per month, ensuring high availability and rapid resolution of critical data platform issues.

Developed robust marketing data pipelines for major platforms including Google, Facebook, and Rakuten, ensuring reliable data flow.

Created core, reusable components in Scala that were widely adopted by other teams, significantly enhancing development efficiency across the platform.

Conducted performance tuning for Spark jobs, optimizing data processing efficiency and reducing execution times.

Achieved significant work process acceleration, surpassing 10x improvements, by implementing asynchronous calls within data pipelines.

Successfully executed and deployed various data jobs, enhancing overall platform functionality and data availability.

Developed real-time and near-real-time pipelines using Flink, Spark, and AWS, ensuring high-speed data ingestion and processing.

Designed and maintained batch ETL pipelines and data lakes on GCP using Airflow, Scala, and Python, ensuring robust data infrastructure.

Conducted performance tuning for Flink and Spark, optimizing data processing efficiency and resource utilization.

Created core, reusable components in Scala that were seamlessly adopted by other teams, fostering consistency and accelerating development.

Implemented efficient Cluster/Jobs monitoring Dashboards, significantly reducing dependency on Stackdriver and cutting cloud usage costs.

Developed intricate ETL pipelines and data lakes using Scala, Spark, Kafka, Hadoop, and Amazon S3 for global data ingestion from various devices.

Created reusable tools using Java, Scala, and Bash scripts, significantly enhancing team efficiency and productivity.

Developed and implemented GDPR-related jobs, ensuring full data compliance with stringent regulatory requirements for Apple Media Products data.

Successfully designed and delivered common data frameworks to downstream teams, actively guiding their effective utilization for advanced analytics.

Led the full-stack development of a critical micro-service, improving user data management and access for various airline services.

Utilized Java 8, Spring Core, Spring AOP, and Spring Security to build robust and secure backend functionalities.

Implemented caching mechanisms with Memcached to optimize data retrieval and enhance system performance.

Collaborated on GitLab/Bitbucket for version control and Jira for project management, ensuring efficient development workflows.

Led the end-to-end implementation of an application for tracking workplace distribution, managing employee allocations, and handling location management.

Developed the application using Java 8, Spring Core, Spring MVC, Spring JDBC, Spring Data, and Spring Security, ensuring a robust and secure architecture.

Implemented CI/CD pipelines and utilized Docker for streamlined deployment and improved development efficiency.

Successfully launched the application into production, achieving widespread adoption across all EPAM offices.