Dmitry Iakovlev
,
test@gmail.com

Experience

Senior Data Engineer
March 2022
 — 
Present
  • Led, designed and implemented real-time and batch pipelines (Apache Beam, Pub/Sub) for high load systems with monitoring, alert systems and dashboards (Grafana, Looker Studio)
  • Owned Python library for Kubeflow (Vertex AI), reducing costs by 80% compared to GCP Cloud Composer (Apache Airflow)
  • Spearheaded E2E testing and performance/load testing for Realtime Dataflow pipeline (Apache Beam) with based on Jmeter and Kubernetes solution, resulting in optimized system performance and costs by 30%
  • Successfully migrated 5 GCP projects into Terraform, reducing deployment times by 60%
  • Brought SWE the best practices: code review, testing, code style, git workflow, revisions
Senior Data Engineer
October 2021
 — 
March 2022
  • Enhanced GSheet import processes into GCP BigQuery, automating column definitions and providing customizable options for seamless data integration
  • Established robust data transfer mechanisms using Airflow and Dataset Transfer Service, facilitating efficient data movement between different regions and ensuring data integrity across platforms
  • Owned cross-communication system between Airflow DAGs, enhancing metadata management and workflow coordination
Stuff Data Engineer / Data Architect
May 2018
 — 
August 2021
  • Engineered Product Information Management system with regular loading data (EAV model, ELT, Apache Airflow)
  • Established monitoring system as a service for several hundreds of RDBMs, Web Application Servers (IIS), Objects Storage service (Grafana, Prometheus, MinIO)
  • Managed Data Lake (MinIO/AWS) supporting CDN and ML applications, optimizing storage efficiency by 30%.
  • Successfully decreased database replication issues by 50% through architectural improvements, ensuring high availability and reliability of critical systems
  • Optimized object storage service; count of number of users has doubled without any service falls
  • Created Release Notes tool to control unmerged Git branches with resolved tasks and to highlight unresolved tasks in version. These types of issues were reduced by 90%.
  • Designed and maintained error log system with subsequent analysis (ELK).
Lead Data Engineer
May 2017
 — 
April 2018
  • Owned migration tool: Migration scripts are generated automatically via Jenkins based on described dataflow in friendly BA tool. After analysis there are no needs developers, therefore process of realization of migration scenarios is cut to the developer tasks.
  • Created Data Pipelines to migrate all data from legacy to the current system
  • Managed a team of 7 DAs to successfully migrate data from customer platform to Netcracker’s solution
Lead Data Engineer
July 2014
 — 
April 2017
  • Developed backend of Financial Reporting System for Deutsche Bundesbank (ETL, Data Pipeline, Apache Airflow)
  • Achieved a 50% reduction in ETL script execution time for a 14TB Data Warehouse, improving data availability and timeliness for reporting and analysis
  • Translated business propositions into quantitative queries and collect/clean/enrich the necessary data
  • Owned new module to identify and control beneficiaries. It helped to exclude manual work and reduce preparation report time from 2 months to 1 week
  • Coordinated a team of 3 data engineers, implementing changes in team processes and ensuring high-quality deliverables through structured code reviews and testing methodologies
Data Engineer
March 2010
 — 
July 2014
  • Implemented internal system for integration with IFRS
  • Developed Automated Banking OLTP system with automation transport of transaction to DWH

Education

Chelyabinsk State University
September 2005
 — 
June 2009
BS in Mathematics
Chelyabinsk State University
September 2009
 — 
January 2012
MS in Economics

Skills

Coding languages:
SQL (Oracle, PostgreSQL, BigQuery), Python, Java , NoSQL (MongoDB, BigTable)
Advanced Techniques:
Cloud Data Engineering, Data Pipeline Automation, Data Architecture, Data visualization
Cloud Computing:
GCP, AWS, MinIO
Technologies/Tools:
Terraform, Grafana, Prometheus, Git, Docker, Kubernetes, Apache Airflow, Apache Beam, Apache Kafka, Apache Spark, Kubeflow