Ihor K, Big Data & Data Science Engineer with BI & DevOps skills

WORK EXPERIENCE

Data Engineer

Apr-2011 To Till now

Project: AWS ELT data pipeline and AWS cloud deployment architecture 

(2022-06 – current)

Project Description: Creation of ELT pipelines deployed on AWS to collect data from e-commerce platforms

Responsibilities:

• architecture design of ELT pipeline that gathers data from e-commerce clients into a single data warehouse.
• using DBT for processing customer data and identifying similar attributes.
• setting up Airbyte connections, developing custom Airbyte connectors, deploying AWS architecture, Terraform scripting
• building custom data management tools, creating data flow security solutions

Tools & Technologies: Python, Airbyte, Kubernetes, AWS EC2, CI/CD, OpenVPN server, AWS Lambda, AWS SQS, Fargate, BigQuery, DBT, Airflow, AWS Cloudwatch, REST API, AWS ECR

Project: Batch and Streaming Data Ingest into DataLake

(2021-10 – 2022-05)

Responsibilities:

• Design data processing pipelines for medical/ marketing/ e-commerce applications.
• Data Modelling,
• Database Design,
• Database development,
• using DBT for processing patient data,
• Big data processing using Spark Scala,
• Distributed platform development,
• ETL Data Transformation
• ETL Architecture and ETL Solutions Design

Tools & Technologies: Python, Scala, DB (SQL, PostgreSQL), DBT, Spark, Hadoop, Terraform, Kubernetes, Helm, GitLab CI/CD, AWS, Keycloak, Swagger, AirFlow.

Project: Audience Segmentation

(2018 - 2021)

Building a custom customer data platform for a marketing company. Build an ETL pipeline that allows retrieving the data from multiple sources and storing them in the private data warehouse in Hadoop. Create CloudFormation "infrastructure as a code" description of the pipeline and CI/CD to deploy it into the desired environment. Work with streaming data in Amazon Kinesis. Design sources for BI reports in AWS.

Responsibilities:

• Design and implement batch and event-driven workflows for big data processing
• Automated tests for distributed applications
• Data analysis and visualization
• Develop applications for data ingestion and selection
• Develop a recommendation system
• Built reporting dashboards in QuickSight from Athena sources.

Tools & Technologies: Python, Scala, SQL, Kubernetes, Spark, Hadoop framework, Docker, AWS (Storage, Database, DocumentDB, Athena, Lambda, Glue, API Gateway, Kinesis, QuickSight, CI/CD AWS CloudFormation and CodePipeline), Grafana, Git.

Data scientist and Data/software engineer

(Jan-2011 To 2018)

• data analysis
• applying machine learning algorithms
• image analysis
• image recognition
• neural networks developing and tuning
• Database development
• ETL operations engineering
• Development of backend services for data curation
• Automated tests for CI/CD workflows

Tools & Technologies: C#, Python, Keras, TensorFlow, Theano, OpenCV, Pandas, Microsoft SQL Server, SQL, .NET Framework,

Associate Professor

(09/1999–Present)

Department of industrial automation

Taught courses:

• Database development
• Database management systems
• Object-oriented programming
• Parallel programming
• System programming
• Development .NET applications

EDUCATION AND TRAINING

• Measurement methods and devices Ph.D. Degree, EQF level 8  
• Master of industrial automation, EQF level 7 

COMMUNICATION SKILLS 

• Communication skills both oral and written gained as a university professor and R&D projects participant
• Presentation skills gained as a scientific conference speaker

COURSES & CERTIFICATES:

• AWS Certified Data Analytics
• HDP Overview: Apache Hadoop Essentials (SPLL)
• Feature Engineering with PySpark
• Big Data Fundamentals via PySpark
• Deep Learning in Python
• Intermediate Python for Data Science
• Linear Classifiers in Python
• Machine Learning with the Experts
• Python Data Science Toolbox