Dmytro Kibets

Scope: Driving financial and competitive data insights for pricing, marketing, financial planning and analytics. Technologies: Python, SQL, Apache Spark, Airflow, AWS, Presto. • Led and mentored several data engineering teams, managing 1,000+ essential financial and growth metrics, that used across 100+ downstream datasets. • Drove data excellence initiatives, ensuring precise allocation of millions in daily spending, optimizing pricing algorithms, and driving revenue growth. • Remodeled the data processing Spark + Airflow pipeline framework, resulting in 30% reduction in time to market for revenue metrics. Additionally, this initiative led to a substantial cost-saving of $300k+ annually through improved cloud resource usage efficiency. • Developed a real-time metric delivery process, reducing data latency from 48 hours to 1 hour, enhancing decision-making speed. • Designed new practices for interviewing processes that reduce time and stress of participants; improved engineering onboarding and orientation.

Scope: Data insights solutions on top of IoT infrastructure for optimization of different geological mining processes. Technologies: Python, SQL, Terraform, Bash, Apache Spark, Google Cloud Platform, BigQuery, Databricks, MySQL, SQL Server, Cloud Functions, Docker, Power BI, Azure. • Developed real-time reporting data models, enhancing mining KPI dashboards for executive teams on 3 operational mine sites, improving decision-making speed and accuracy. • Designed data models and feeding pipelines for an experimentation application utilized by over 20 geological scientists. • End-to-end designed and implemented a data migration assessment toolkit to load and assess bidirectional replication between 2 OLAP DBs. The toolkit was reused for several migration initiatives, saving approximately 1 month for each migration spin-up. • Led cloud migration from GCP to Azure, improving scalability and reducing operational overhead. • Developed an SQL translator tool enabling seamless migration of 10,000+ workloads between Teradata and BigQuery, accelerating migration timelines.

Scope: Implementing advertisement impressions and spending reporting on top of real-time advertisement bidding auctions system. Technologies: Python, SQL, Apache Spark, Airflow, Amazon Kinesis, AWS, Snowflake, S3, Looker, Docker. • Redesigned the data pipeline testing framework, achieving 20% faster deployment cycles and boosting team productivity. • Remodeled DS experimentation notebooks into automated metrics generating pipeline, enabling deprecation of redundant compute infrastructure, saving $10k/year. • Successfully onboarded 10+ impression dimensions, improving ad targeting analytics and enhancing advertiser satisfaction.

Scope: Designing pipelines for Government Pricing reporting. Technologies: Python, Apache Spark, Pandas, JS, HTML, CSS. • Developed a unified JS reporting templating system, reducing template creation time from 4 hours to 20 minutes, benefitting 30+ clients with 300+ pricing reports. • Owned and maintained Pandas/Spark pricing pipelines, servicing 20+ clients, ensuring data accuracy and timely delivery. • Led a team of 4 engineers through the technical onboarding and delivery process, facilitating seamless integration for clients. • End-to-end designed and implemented an automated code versioning system, bridging the gap between local environments and the cloud platform, saving approximately 2-4 hr/week for each of 5 engineering teams.