What you will do:
- building ETL processes: developing and configuring pipelines for collecting and transforming data from various sources;
- working with a modern transformation stack: learning and applying DBT to turn "raw" data into clean analytical views;
- data orchestration: configuring and supporting workflows (pipelines) in Dagster to ensure data in reports is updated on time and without failures;
- storage management: working with Postgres and the high-performance columnar database Clickhouse to ensure report speed;
- meaning visualization: developing interactive BI reports in Apache Superset that will help the business make data-driven decisions;
- sample optimization: analyzing and accelerating heavy queries to improve the performance of the entire reporting system.
What we expect from you:
- SQL: confident command of the language (complex queries, aggregate functions, understanding of query optimization principles);
- databases: understanding the structure of relational databases (specifically PostgreSQL) and general principles of building analytical data warehouses;
- Python: basic knowledge of the language, ability to write scripts for data processing and automation of simple tasks;
- analytical mindset: ability to work with large datasets, identify patterns, and structure information.
Desirable experience:
- BI tools: experience in creating dashboards in Apache Superset, Tableau, Power BI or similar systems;
- ETL/ELT: understanding of data extraction, transformation, and loading concepts;
- data architecture: understanding the difference between OLTP and OLAP systems (e.g., why Clickhouse is used for analytics);
- modern stack: interest in DBT (Data Build Tool) or data orchestrators (Airflow, Dagster).
We offer:
Fixed-term employment contract for 3 months, 5/2, no more than 30 hours per week, remote/office/hybrid.
We consider students from any university, any form of study, including part-time, including Master's students for the internship.
