A large USA enterprise company which supplies IoT sensors for different assets. Analytical Platform includes many metrics about trucks, boats and other assets, such as Dwelling, Media Activity, Geofence, etc. The task is to synchronise the data to process and build dashboards for business based on them. And also give some processed business data in the form of data marts and statistics on them. Based on the received solution, sales were analysed and market development strategies were changed.Responsibilities:- Data engineering- Database administration- Integration with other services- Managing OpenSearch Indexes- Building ETL pipelines using Apache Airflow- Collaborating with business analysts and data architects to ensure that data models align with business needs and data architecture- Conducting data profiling and analysis to ensure data accuracy, completeness, and consistency- Building PySpark pipelines with AWS Glue- Performance optimization- Ensure data acсuracy and flexibility- Create partitions on SQL tables- Writing complex SQL queries, procedures and triggers for PostgreSQL- Test SQL databases and perform bug fixes- Data processing using AWS Databricks- Implement serverless calculations using AWS Lambda- Code review and refactoring

Cutting-edge conversational AI chatbot designed to enhance telecommunication services. The botimproves user interactions by skillfully understanding input, employing machine learning through theRASA Framework(based on TensorFlow), and providing intelligent responses guided by predefined rules.Analytical platform gives a wide understanding on how users interact with service, what amount ofmoney has been saved and how business can improve main KPIs.Responsibilities:- Administrating internal and cloud databases- Development and optimization of complex SQL queries and Stored Procedures- Crafting and implementing API service, facilitating communication between the chat bot and external services(analytics mainly)- Building and designing Grafana dashboards based on main business KPIs- Creating of TTL Indexes in MongoDB- Conducting analysis of user interactions through log data, extracting insights to continually refine the bot's performance- Creating ETL/ELT pipelines with AWS Glue- Query development and optimization of Elasticsearch Queries- Implementing Log data Dashboards using AWS ELK stack(ElasticSearch, Logstash, Kibana)- DWH design and implementation in AWS Redshift- Managing MongoDB collections- Optimising response time with cache strategy with Redis- Communication with business stakeholders about system requirements- Creating Bash scripts for automation- Identifying and excluding anomalies from the dataset- Validating data transformations and migrations- Conducting data cleansing in order to get best quality of the data- Utilising Kubernetes to automate deployment processes, ensuring effective management ofapplications in distributed environments

A retail analytics platform that allows businesses to track and optimise their sales performance. Theplatform integrates data from various sources such as point-of-sale systems, inventory managementsystems, and customer loyalty programs. This data is processed through ETL pipelines and stored in acentralised data warehouse. The platform then generates interactive dashboards and visualisationsthat provide insights into sales trends, inventory levels, customer behaviour, and marketingeffectiveness. Responsibilities:- DWH design development- Analysing business requirements and translating them into technical data models- Dimensional data modelling with Power BI- Building effective end-to-end ETL pipelines using Data Factory- Transforming data using Azure Data Factory- Writing and optimising complex SQL queries- Setting up backups for Azure SQL databases- Analysing data and dealing with missing values with Pandas- Performing data quality checks including preparing test cases, checklists- Establishing and organising systems and database objects for maximum efficiency and functionality- Exploratory data analysis