Vladyslav Vinnyk

* Designed and implemented a self-service Data Lifecycle platform for Databricks to manage TTL retention and external Delta table maintenance across all Lyft offline tables, purging 10PB of redundant data daily. Projected Impact: This architecture enabled a shadow migration strategy that is forecast to save $4.5M, with ongoing automated maintenance expected to yield an additional $2.7M.

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

Engineered a custom cold data detection engine that delivered an immediate $500,000 one-time storage savings alongside ongoing annual reductions of at least $500,000.

Resolved a critical scalability bottleneck in the legacy offline table retention framework, increasing throughput by 30x, guaranteeing a strict 10-hour SLA, and generating $415,000 in annual storage savings.

Led Hive Metastore to AWS Glue data catalog migration efforts; implemented a user action ingestion pipeline that accelerated table user detection by 5x and eliminated metadata bottlenecks, yielding an 86x write and 100x read performance improvement.

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) while safely de-risking legacy system modernizations.

Designed and deployed a table-level cost attribution engine that scans S3 objects using custom heuristics, providing precise compute and storage cost visibility across the entire Data Platform to streamline cross-functional budgeting.

Led research initiatives on cutting-edge technologies, delivering proofs-of-concept and strategic recommendations that informed platform enhancements. Designed and maintained critical system components, including orchestration, monitoring, table maintenance (compaction, snapshot cleaning), and data validations, while leading design enhancement meetings.

Addressed complex 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, which made it possible to ingest ad data from 10 different sources, comply with SLA, and make attribution much more accurate.

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 microservice, 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.