Senior Software Engineer — TetraScience
Jan 2026·present
- Platform engineering. Continue building and operating production data pipelines, validation workflows, and deployment tooling for pharma and biotech customers on the TetraScience platform.
01
Recent experience
Hands-on work across lab data platforms, large migrations, productized tooling, and team quality practices.
Data Engineer — TetraScience
Apr 2024·Jan 2026
- Lab data pipeline (top-10 pharma). End-to-end ingestion, standardization, and labeling for scientific instruments with delivery into enterprise ELN/LIMS — roughly $400k/year operating savings and better access for scientists.
- Data migration & integrity (global pharma). Multi-step framework to migrate ~10 TB between S3 environments with custom checksums and reconciliation; zero data-loss migrations.
- Secure data app modernization (biopharma). FastAPI app upgraded with SSO (OAuth2/OIDC), CSRF/CORS hardening, optimized Elasticsearch queries, and local caching for reliability and latency.
- QC automation at scale. 18 production-grade data apps/pipelines replacing manual work; introduced Git-based versioning and lightweight CI in a change-resistant environment.
- Instrument onboarding (R&D). Full onboarding: extraction, transformation, and storage pipelines for downstream analytics and reporting.
- Schema evolution. Extended models and pipelines for new instrument/assay outputs so new experimental data could be analyzed.
- Validation DSL. Domain-specific language (Yacc/Lex) and Python library to compile rules and validate JSON payloads from S3 uploads; outputs integrated into Snowflake for automated data quality checks.
- Terraform provider. Custom Terraform provider (Go) to define workflows in YAML and promote across environments for one-click use-case deployments.
- Engineering quality. CI/CD quality gates and code reviews to improve reliability and maintainability.
Data Engineer — Playrix
May 2022·Jun 2023
- AWS & Python ETLs. ETLs from Redshift, S3, DynamoDB, and APIs using Fargate; transformed and aggregated data for Tableau dashboards.
- Data duty & monitoring. Owned monitoring and incident response for sparse ETL processes; data integrity through alerts and swift error handling.
- Ad-hoc analytics. Timely analysis with AWS (Redshift, Fargate) and Python (Luigi, pandas, NumPy) for internal stakeholders.
Data Scientist / Engineer — NSC
Oct 2019·Jun 2022
- Behavior-based predictions. Airflow DAGs integrating MinIO, ClickHouse, and MongoDB to predict gender and age from TV viewing behavior.
- Session-content matching. Fast mapping of content air time and viewing periods for session-like primary data sources.
- Video content evaluation. PyTorch deep learning for video vectorization and a complex ETL for user-behavior insights.
- Client delivery. Cross-team collaboration and analytics outcomes for internal and external clients.
Analyst — Luxoft
Jan 2017·Jan 2018
02
Key tools
Stack I use most often for pipelines, platforms, and data stores.
Languages
- Python
- Rust
- Golang
- SQL
Cloud
- AWS
- GCP
ETL / ELT
- Airflow
- Luigi
- dbt
Frameworks
- FastAPI
- Terraform
Databases
- Redshift
- PostgreSQL
- MySQL
- Snowflake
- MongoDB
- ClickHouse
- Elasticsearch
Delivery
- Git
- Tableau
03
Academics
Teaching alongside industry work.
Lecturer — Big Data Analysis and Storage Tools
- Hands-on perspective on big data ecosystems (Spark, HDFS, Kafka); course noted for practical novelty.
04
Education & certificates
Formal training and credentials.
Moscow College of Informatics and Computer Engineering
Moscow, Russia · Associate's Degree, Computer Software Engineering · 2012 – 2016
Netology
Moscow, Russia · Advanced training: product management · 2021 – 2022
Certificates
- AWS Certified Solutions Architect — Associate Nov 2023 – Nov 2026
- Applied Data Science with Python Specialization, University of Michigan via Coursera Aug 2018
- IELTS English C1 2023
