Position: Data Engineer
Category: Data Engineer
English level: intermediate
Years of experience: 0.5

- I'm analitical, self-confident and responsible person. 
- I've great communication and negotiation skills. 
- Graduate of EPAM Data Engineering Lab. 
- I'd the opportunity to work with SQL MS, MYSQL, ELT//SSIS, Windows Forms.
- I've experience with such programming languages  as C#, T-SQL.
- Basic knowledge of implementation of ETL solutions using on-premises software (SSIS) 
- I've experience cloud-based services (AWS and GCP) 
- Basic Python skills 
- One of my goals is to continually improve myself
Oct, 2021 – Jul, 2022
Job Position: Trainee Data Engineer
Project Roles: Trainee Data Engineer 
Worked with various tools and technologies:
- wrote SQL queries;
- wrote Python code;
- worked with Git and GitHub;
- worked with Amazon Web Services;
- created S3 bucket, Lambda function, DynamoDB table, SNS, AIM, EC2;
- worked with Google Cloud Platform;
- created Cloud Storage, BigQuery, Composer, Cloud SQL, Cloud Functions; - developed DAGs in Airflow;
- developed Dashboard in Tableau;
- developed Report in PowerBI;
- worked with Hadoop, Hive, Spark.
Database: SQL Server 
Tools: SQL Server, Pycharm, Jira , Visual Studio 2019, SQL Server Integration Services (SSIS), AWS, Power BI Desktop, Tableau.
Desktop Technologies: SQL, ETL with SSIS, AWS, GCP, Tableau, PowerBI, Airflow, Hadoop, Hive, Spark.

Jul, 2022 – Nov, 2022
Job Position: Data Engineer
Project Roles: Data Engineer
Project: Data is stored in CSV files. The CSV files uploaded into Google Cloud Storage named Buckets. From the Buckets CSV files are migrated into Cloud SQL (SQL Server or PostgreSQL or MySQL). Here we build relationships between tables and bring to the third normal form. Next step set up workflow with using Cloud Composer for orchestration. On the Cloud Composer we built on the Apache Airflow and operates using the Python programming language. By using Airflow and Python we are going to create Data Warehouse in the Cloud BigQuery with arranging all staging and aggregation steps on this database. The whole workflow should be built to maintain full load and incremental loads. On Cloud BigQuery in very last step we will create views with data prepared specifically to reports. For the reports can be used any of tools Tableau/Power BI/Looker.Participation- Create a Cloud Bucket and uploaded CSV files and JSON in there; - Create a Cloud Funcion that runs when a CSV file is uploaded to the bucket and uploads the data to Cloud SQL; - Migrated required data from Cloud SQL to BigQuery; - Local work with AirFlow and create pipelines - Analysed raw data in MySQL Workbench and selected those necessary for generating reports;Team Mentors team: 2 members, Dev team: 4 members Database MySQL, MS SQL Server, BigQuery Tools Cloud Storage, Cloud Function, Cloud SQL, BigQuery, MySQL Workbench, GitHub, Jira, AirFlow Technologies Google Cloud Platform.