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Taras Konchak

@taraskonchak

Senior Data Engineer at ALLSTARSIT building an MLOps platform for 15 models and automating production deployment.

At ALLSTARSIT, I led development of a production MLOps platform for 15 ML models, automating training, validation, and deployment. This reduced model deployment time from 1–2 hours of manual work to 5–20 minutes.

At N-iX, I led a team of five engineers and built an enterprise data warehouse integrating 10 source systems and more than 120 tables. I also designed real-time Google Cloud integrations and data infrastructure with Terraform.

Earlier, at Epam Systems, I developed data collection and ETL pipelines for financial data, reducing pipeline execution time by 30% through asynchronous Python processing. At Softserve, I built backend microservices and data-processing workflows for an enterprise platform.

Experience

Work history, roles, and key accomplishments

Current

Senior Data/MLOps Engineer

Current

ALLSTARSIT

Jan 2026 - Present (9 months)

Led development of a production MLOps platform for 15 ML models, automating training, validation, deployment, and serving updates. Reduced model deployment time from 1-2 hours to 5-20 minutes and optimized Databricks infrastructure to cut compute costs by 40-50%.

Python PySpark Databricks MLFlow GitHub Actions Databricks Asset Bundles Unity Catalog GCP Google Datastream Terraform CI CD MLOps Infrastructure as Code CDC Real time Data Processing

Senior Data Engineer

N-iX

Dec 2022 - Jan 2026 (3 years 1 month)

Led a team of 5 engineers, architected event-driven real-time integrations on GCP, and built an enterprise data warehouse using dbt. Designed data projects from scratch and provisioned cloud infrastructure with Terraform.

Python Airflow Snowflake DBT Terraform Atlan Monte Carlo AWS GCP BigQuery Dataflow Pub Sub ETL ELT Data Warehouse Real time Data Processing

Software Engineer

Epam Systems

Apr 2020 - Dec 2022 (2 years 8 months)

Developed a high-performance data collection library for financial institutions, focusing on asynchronous background workers and data pipelines. Reduced pipeline execution time by 30% by introducing asynchronous data processing with Python.

Python AsyncIO Pandas AWS Redshift PostgreSQL MongoDB MySQL ETL CI CD

Software Engineer

Softserve

Aug 2019 - Mar 2020 (7 months)

Built and maintained backend microservices architecture for a large enterprise platform. Implemented asynchronous and distributed processing components using Python, RabbitMQ, and Celery.