#vacancy #DataAnalysis #dataanalyst #JOB #remotework #RU #FullTime
Project
The company creates and implements innovative IT solutions for processing financial data, preparing deep analytics, and delivering content to end-users.
Conditions
- Employment: full-time
- Location: Russian Federation
- Citizenship: Russian Federation
- Working hours: Moscow time ± 2 hours
- Format: Remote
Position
- Role: Data Analyst / DWH Analyst
- Level: Senior
Required
- Data analyst experience of 5 years or more;
- Experience in long-term projects (1-1.5 years or more) in the resume;
- At least 3 years of commercial experience in companies with a strong data culture;
- Experience with Apache Airflow in a corporate environment: configuring and managing data pipelines, developing DAGs in Python for automating ETL processes, optimizing and monitoring DAG execution;
Experience with the stack:
- Python
- S3 storage,
- Kafka streaming,
- DBMS Postgres, MSSQL, MongoDB, Clickhouse;
- Experience in developing ETL/ELT processes based on business requirements with an understanding of data transformation and integration;
- Knowledge of data catalog principles;
- Experience in designing and creating data models, adapting them for analytical tasks and efficient storage operation;
- Understanding of data stream processing (Kafka, Spark Streaming): experience with Kafka – configuration, optimization, and scaling are mandatory, deployment is desirable;
- Confident use of SQL for complex queries and performance optimization (window functions, CTEs, database mappings);
- Deep knowledge and experience with relational databases: understanding data structures, methods of data population and management;
- Skills in working with version control systems (Git).
Desirable
- Experience in FinTech;
- Experience in setting up CI/CD for data processes;
- Experience with cloud platforms (Yandex.Cloud);
- Experience or understanding of the stock market and investment instruments.
Tasks
- Organization and automation of orchestration processes with Airflow: launch, monitoring, alerts, management of task and DAG dependencies;
- Design and optimization of data transfer flows from MSSQL to a new storage;
- Analysis of data sources in legacy MSSQL: collecting information about content, identifying key business entities and dependencies, scheduled operations;
- Ensuring quality, data testing, and integrity control during migration and transformation;
- Development and support of data models and transformations in dbt for forming final data marts;
- Implementation and maintenance of incremental load processes and performance optimization;
- Working with data streams and integration with Kafka for real-time event exchange.
**For all questions @Goose0005
