Iuliia Volkova
Head Of Data Engineering
St Petersburg, St Petersburg City, Russia
Summary
🤩
Rockstar🎓
Top SchoolIuliia Volkova is a Head of Data Engineering and seasoned Python engineer with 10 years of experience building and leading backend and data teams across startups and large enterprises. She has deep expertise in orchestrating ETL pipelines and data platforms using Apache Airflow, BigQuery/GCP, PostgreSQL/ClickHouse, and cloud-native stacks on AWS and GCP, and has contributed backend fixes and new BigQuery/GCP ML Engine hooks to the popular Apache Airflow project. Known for migrating legacy Python codebases to modern tooling and improving DWH performance, she combines hands-on development (FastAPI, SQLAlchemy, testing, CI/CD) with team leadership, mentoring, and developer relations. Currently leading Data Engineering at CodeScoring in St. Petersburg, she brings a pragmatic focus on static analysis and robust infrastructure while also driving developer productivity through internal communities and education.
9 years of coding experience
11 years of employment as a software developer
Незаконченное высшее Прикладная информатика в экономике, Незаконченное высшее Прикладная информатика в экономике at Moscow State University of Economics, Statistics and Informatics (MESI) Saint Petersburg State University of Aerospace and Instrumentation
Русский, Английский
Github Skills (15)
apache-airflow10
google-cloud-platform10
orchestration10
orchestra10
bigquery10
python10
data-engineering10
gcp10
data-pipelines9
data-pipeline9
workflow-engine9
sql8
testing7
automations7
automation7
Programming languages (4)
Github contributions (5)
apache/airflow
Feb 2018 - Jul 2020
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Role in this project:
Backend Developer
Backend DeveloperContributions:21 PRs, 117 comments in 2 years 5 months
Contributions summary:Iuliia primarily contributed to the backend infrastructure of Apache Airflow, addressing bugs and implementing new features within the BigQuery and GCP ML Engine hooks. They updated code to handle schema updates, add new API configurations, and support autodetect schema functionality. The contributions also include the creation of new operators for dataset management and the addition of methods for retrieving datasets.
monitorpythonschedulerapacheprogrammatically
xnuinside/airflow_in_docker_compose
Jul 2019 - Dec 2022
Apache Airflow in Docker Compose (for both versions 1.10.* and 2.*)
Contributions:1 review, 38 commits, 32 PRs in 3 years 4 months
docker-compose-templatedockerapachecomposedocker-compose
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