Evgeny Shebanin
I am a data engineer with extensive experience in designing core and customer-extended data warehouses to enable financial, management, and customer-facing reporting.
About
I am a data engineer with extensive experience in designing core and customer-extended data warehouses to enable financial, management, and customer-facing reporting. I work across source systems, database engineering, and performance tuning to meet real-time and complex analytics requirements, with a strong focus on banking data integration, payment screening, and large-scale data platforms. I combine hands-on technical skills with business insight to deliver reliable, data-driven systems that support decision-making across industries.
I am proficient in SQL Server, Snowflake, Oracle, and cloud data platforms (GCP, AWS, Azure), with deep expertise in PySpark, Python, DBT, and DevOps practices to deploy scalable, compliant data pipelines. I enjoy collaborating with cross-functional teams to translate complex data into actionable insights and to build robust data architectures that scale with business needs.
- Joined November 2025
Languages
- EnglishAdvanced
- FinnishBeginner
- RussianFluent
Industry experience
- Software & Internet
- Financial Services
- Professional Services
- Other
Work experience· 15 roles
BI platform design for medium businesses, including product concept and technical stack. Defined microservices (Docker, Kubernetes), AirFlow and Python-based ingestion, GCP datalake, ClickHouse as the data warehouse, and Metabase for BI. Led Docker infrastructure design, designed operational data stores for non-SQL sources, ingested pipelines, and built a GCP datalake. Created Metabase dashboards and established a scalable data platform. Current role with complete product rollout and initial implementations.
Maintained Ctrl-M jobs for QlikSense and AWS Glue applications. Set up Ctrl-M jobs, designed AWS Glue jobs with PySpark, configured AWS Athena, implemented GitHub Actions for CI/CD, and prepared AWS infrastructure (S3, Lambda). Developed Lambda with PySpark logic and implemented a data quality framework to support robust releases.
Migrated data pipelines from Python-based AWS EC2 deployments to Databricks. Engineered Databricks pipelines, created service accounts, and developed Azure DevOps pipelines for deploying bundles. Refactored PySpark pipelines for multi-use and multi-environment setups and established multi-user configuration in Azure DevOps. Designed Azure SQL for interim storage of trial data.
Education
Certifications and awards
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