Position: Data Engineer
Category: Data Engineer
English level: upper
Years of experience: 4


Apr-2022 – Now(2023) - Big Data Software Engineer, SoftServe, 
Project: Implementing Data Warehouse in BigQuery
Team Size: Dev team - 5 DQ team - 1 DevOps - 2 Support - 2 PM - 1
Project Role: Developer
Tasks performed:
•	Support data pipelines running on production environment
•	Implement new data pipelines from various sources (SFTP/API/RDS)
•	Migrate old legacy pipelines to be more flexible/extendable and runnable on GCP ecosystem
•	Resolve incidents/change requests; communicate with requestors and users on daily basis
Environment:
•	GCP BigQuery
•	GCP Composer
•	GCR, GCS, GSM, Cloud Function

Nov-2021 - Mar-2022 - Big Data Software Engineer , EPAM Systems

Customer: EV - Other
Project: Augment client's team to build data lake in Redshift
Team Size: Dev team - 3 DQ team - 3 DevOps - 2 PM - 1
Project Role: Develop
Tasks performed:
•	Develop new ETL jobs.
•	Supporting old jobs, enhancing them with new requirements.
•	Communicating requirements with clients.
•	Refactoring solutions and proposing improvements, such as code, libraries, tools, etc.
Environment:
•	Redshift
•	AWS, Python, Serverless, Jenkins
•	AWS Lambda, EC2, S3, Glue, Athena, Redshift, AppSync

Feb-2020 - Oct-2021 - Big Data Software Engineer, EPAM Systems
Customer: EV - Other
Project: Reimplement Cloudera-based Big Data system in AWS
Team Size: Dev team - 3 DQ team - 3 Automations - 1 DevOps - 2 Support - 2 PM - 1
Project Role: Developer
Tasks performed:
•	Take an unstable on-premises PoC system and create a scalable production-ready platform within a cloud;
•	Delivery of API extensions to support numerous Customer’s Data Analytics applications;
•	Exponential system loads at the harvest time;
•	Use of variety of sources for genotype & phenotype data (varying from 10 rows to bills of recs);
•	Conduct technology assessment, select and deploy cloud-based toolkit for data scientist to extract high-volume data sets and run analytic & machine learning workloads;
•	Provide system stability during peak workloads (harvest seasons);
•	Contain operational costs;
Environment:
•	AWS - Aurora, Redshift, Glue Postgres for metadata and Airflow
•	AWS, Python, Airflow, Serverless, Jenkins
•	AWS Lambda, EMR, EC2, S3, Glue, Step Functions, Athena, Redshift, AppSync, Event Bridge Airflow, Postgres.