Artem Demin

About Artem Demin

Experienced Data Engineer with a Master’s degree from Bayes Business School (London), specializing in building scalable data pipelines, distributed data systems, and AI-driven automation solutions.My technical proficiency includes:• Data Engineering: Designing and building scalable data pipelines, ETL/ELT workflows, and large-scale data processing systems.• Data Pipeline Orchestration: Building and scheduling data workflows using tools such as Apache Airflow and modern automation frameworks.• Streaming Data Systems: Developing real-time data ingestion pipelines using technologies such as Apache Kafka and event-driven architectures.• Distributed Data Processing: Processing large datasets using distributed computing frameworks such as Apache Spark and scalable data processing techniques.• Database Engineering: Designing scalable relational and analytical databases, data modeling, schema design, indexing strategies, and SQL performance tuning.• Data Warehousing: Building modern analytical data warehouses and integrating structured and semi-structured data for analytics.• Programming: Extensive experience in Python and R for data engineering, automation, and machine learning pipelines.• Machine Learning: Developing supervised and unsupervised algorithms and deploying ML pipelines.• Transformer and LLM Tuning: Fine-tuning transformer architectures and large language models.• Data Visualization: Creating analytical dashboards using Power BI, Tableau, and QlikSense.• Business Automation: Developing AI-driven workflows, automation systems, and intelligent bots to streamline business processes.• Web Development: Building data applications and APIs using FastAPI and React.

Experience

1. Senior Data Engineer

   Vertex Inc.

   Jan 2025 — Present · Miami, FL, US

   Built Python and SQL ETL pipelines orchestrated with Airflow processing more than 5 million transactional records per month.• Designed transformation models using dbt improving analytics dataset refresh time by 60 percent.• Integrated event driven ingestion pipelines using Kafka enabling near real time processing of operational events.• Developed analytical datasets optimized for Redshift reducing complex query execution time by 45 percent.• Implemented automated data validation checks improving data quality coverage across pipelines to more than 98 percent.

Education

• The London School of Economics and Political Science (LSE)

  Bachelor's degree, Management and Digital Innovation

• Higher School of Economics

  Bachelor's degree, Business-informatics

• Bayes Business School

  Master's degree, Business-analytics