Contact information

You can get this candidate's contact information from https://www.work.ua/resumes/19937617/

Work experience

Lead Data Engineer

from 02.2023 to 07.2026 (3 years 6 months)
Instacart, Remote (Retail)

•Led development of a real-time grocery marketplace data platform, supporting CDC ingestion, event processing, and warehouse-ready data delivery at scale.
•Built and optimized Kafka-, Spark-, and Airflow-based pipelines to transform high-volume operational data into trusted analytics datasets.
•Designed dbt models and data validation checks to improve dataset consistency for analytics, experimentation, and retailer reporting.
•Improved pipeline reliability through monitoring, automated testing, and faster root-cause analysis of production data issues.
•Partnered with product, analytics, and engineering teams to deliver clean, timely datasets for business reporting and operational decision-making.
•Prepared ML-ready data layers and feature datasets to support personalization, prediction, and marketplace optimization use cases.
•Provided technical leadership by reviewing designs, mentoring engineers, and enforcing maintainable data engineering standards across the platform.

Senior Data engineer

from 06.2021 to 01.2023 (1 year 8 months)
FluX.AI, Remote (IT)

•Designed and developed scalable data engineering platforms supporting AI-powered hardware design workflows, managing complex engineering datasets including PCB designs, component libraries, simulation results, and real-time collaboration data using Python, SQL, Apache Spark, and cloud data technologies.
•Built advanced ETL/ELT pipelines to ingest, transform, and process high-volume engineering data from CAD systems, IoT devices, APIs, and cloud storage platforms, enabling reliable analytics and AI model training workflows.
•Architected cloud-native data lake and warehouse solutions using AWS S3, Snowflake, Google BigQuery, Azure Data Lake, and Databricks to support large-scale engineering analytics, design optimization, and machine learning applications.
•Developed data pipelines for AI/ML and Generative AI systems, including feature engineering workflows, embedding generation pipelines, vector database integration (PostgreSQL + pgvector), and RAG architectures supporting AI engineering assistants.
•Implemented real-time data processing and monitoring solutions using Apache Kafka, Spark Streaming, Airflow, Kubernetes, Docker, and CI/CD pipelines to improve system reliability, scalability, and automated deployment of production data services

Data Engineer

from 08.2018 to 06.2021 (2 years 11 months)
Genpact, Remote (IT)

•Developed and maintained scalable ETL/ELT data pipelines using Python, SQL, Apache Spark, and Apache Airflow to integrate enterprise data from inance, healthcare, insurance, supply chain, and customer operations systems.
•Designed and optimized cloud-based data platforms using AWS, Microsoft Azure, Google Cloud Platform (GCP), Snowflake, and BigQuery to support analytics, reporting, business intelligence, and machine learning workloads.
•Built automated data ingestion and transformation frameworks by integrating structured and unstructured data sources through APIs, databases, enterprise applications, and cloud storage environments while improving data reliability and processing efficiency.
•Implemented big data processing solutions using Spark, Databricks, Kafka, and distributed computing frameworks to handle high-volume operational datasets and enable real-time analytics for enterprise decision-making.
•Developed AI-ready data pipelines supporting machine learning and Generative AI applications, including feature engineering, document processing, embedding generation, vector search, and RAGbased enterprise knowledge systems using LangChain, LlamaIndex, PostgreSQL + pgvector, and LLM APIs.
•Established enterprise data governance, quality, and DevOps practices by implementing data validation, monitoring, metadata management, CI/CD automation, Docker, Kubernetes, and cloud deployment processes to ensure secure, scalable, and production-ready data platforms.

Software Engineer (Data Infrastructure)

from 09.2015 to 08.2018 (3 years)
Celonis Munich, Remote (IT)

•Engineered data infrastructure components for processing high-volume event data, including ingestion pipelines, transformation workflows, and data quality validation services.
•Implemented scalable backend solutions with Python, SQL, Kafka, Spark, and Airflow to support reliable enterprise data processing and analytics workflows.
•Improved pipeline stability by identifying bottlenecks, debugging failures, and optimizing distributed data processing jobs in collaboration with cross-functional engineering teams.
•Strengthened platform maintainability by contributing to automated testing, documentation, and clean-code practices within an agile software engineering environment

Education

Odesa Polytechnic National University

Computer Science, Odesa
Higher, from 2011 to 2015 (4 years)

Bachelor of Science in Computer Science, with a strong foundation in software development, programming, databases, algorithms, data structures, and computer systems. Completed the degree with a final grade of 9.30/10 between October 2011 and September 2015.

Additional education and certificates

No additional education or certificates.

Knowledge and skills

• Python
• SQL
• PySpark
• C#
• Java
• C/C++
• JavaScript
• Apache Spark
• Kafka
• Spark Streaming
• Airflow
• Dbt
• Feature Engineering
• ML Pipelines
• Generative AI
• RAG
• Vector Da
• PostgreSQL
• MySQL
• SQL Server
• Pgvector
• ETL/ELT
• Data Pipelines
• CDC
• Data Modeling
• Data Quality
• Data
• AWS
• Azure
• GCP
• Databricks
• Snowflake
• BigQuery
• Delta Lake
• Docker
• Kubernetes
• CI/CD
• Monitoring
• Cloud Deployment
• .NET Core
• ETL
• Productivity
• Git
• Conflict-free
• HTML
• CSS
• Manual testing
• Knowledge of OOP principles
• Writing bug reports
• Jira

Language proficiencies

English — advanced