Position: Database [administrator architect] SQL NoSql  BigData ML DL QA
Category: SQL
English level: intermediate
Years of experience: 11

Проектирование системы потоковых вычислений на базе 512 Rasberry Pi4 для использования Cluster Beowulf.
Поддержка и мониторинг PostgreSql MySql  баз в нагруженных проектах. Оптимизация быстродействия RDBMS.
Разработка приложений на основе библиотек Keras, Scikit-learn.
Db Engineer.
Monitoring, support for MySql Perl fast payment service.
Monitoring AWS Python Prometheus services.
DevOps support - environment deployment.
Working with Athena, S3, Glue, App Runner, EC2, Auto Scaling, RDS, Redshift, Created a Docker container for microservices.
Written RegEx for Logstash.
Created Zabbix metrics and dashboards.
Wrote a Python script OpetCV.
Wrote a Python script with scikit-learn and Seaborn for backoffice. Wrote a Python WebScraping model.
Monitoring and support of 10 production PostgreSql servers and 23 dev servers. 
PostgreSql 9.5 10 11 12 13.
Query optimization - explain. Database migration and restoration. Porting the database from the server to the Azure PaaS service and setting up access and monitoring in the ecosystem.
Working with Azure Monitor, Azure for PostgreSQL.
Development of a monitoring system with AI elements.
Attestation training.
Working with Azure Data Explorer, Databricks, Anomaly Detector.
Build of queries for sampling data from the OLAP cube and database to create a scoring system. Calculation of statistical parameters of data from requests.

Design, installation, support: PostgreSql, MySql, Redis, MongoDb. Extensions TimescaleDB, PipelineDB, pg_stat_statements, hstore. Setting up replication, Upgrade database version. Use of systems of high availability of services / balancing sharding partitioning /.
Deploying instances in the clouds AWS, Azure, Google. 
Working with services: EC2 - S3 - Athena - Redshift - AutoScaling.
Working with services: Azure Monitor - Azure for PostgreSQL - Data Factory - Azure Functions - ServiceFabric-Database. 
Migration Service - Data Explorer - StreamAnalytics-Anomaly Detector - Databricks. 
Load testing tSQLt, SQLQueryStress. 
Explain source profiling. 
Porting - PostgreSql to Azure PaaS.
ОLAP - OLTP - DWH- DataLake. 
Monitoring with: Zabbix, Prometeus, Percona Monitoring, pgCluu.
Development of streaming monitoring with AI elements. 
Use of Scikit-learn - regression, search for anomalous data, datacleaning, classification, clustering, data dimensionalityreduction,signal classification and prediction. 
Programming in languages Perl, Python, SQL, Bash, C. For graphical presentation of data, I used - Seaborn.
Introduction to technology: Apache Nifi, Kafka. 
Courses: Cloud Guru: - Linux – Azure.
Команда. 
Перспективы. 
Финансовая стабильность.