The Data Platform DWH enables product warehouse leads for Taxi, Delivery, and other services, data partners, and engineers to understand what's happening with data: track its usage, quality, timeliness, and SLA compliance, control calculation and storage costs, and optimize them. You will be responsible for developing the Data Platform DWH product and overseeing its key functions. This will require combining the roles of a systems analyst and an engineer: designing meta-models, populating them with data, expanding, and modernizing the tool for effective DWH management. You will work with technologies like ClickHouse, YTsaurus, Greenplum, Flink, treating them as both the subject domain and a product for our DWH.

If you are not afraid of complex tasks and are ready to dive into the specifics of heterogeneous data storage and processing systems — come join us.

What tasks await you

Requirement analysis and design You will gather and analyze requirements from various DWH teams to understand their pain points and design meta-models that solve data visibility and manageability issues, enabling analysis of computation and storage costs in the DWH.

Development and visualization You will implement models in ETL/ELT processes — from data collection to creating final data marts, and based on these marts, design and build dashboards in DataLens.

More about analytics at Yandex — in the channel Yandex for Analytics

We expect you to

• Know how to identify, decompose, and formalize requirements from users and technical teams
• Understand the principles of building data warehouses, star schemas, Data Vault, their pros and cons
• Have excellent knowledge of SQL and understand database theory
• Be proficient in Python for solving engineering tasks
• Have experience implementing ETL processes
• Are not afraid of difficult tasks, willing to understand the architecture of data storage systems (ClickHouse, Greenplum, YTsaurus, and others) and dive deep into complex technical processes to grow data as a product

Will be a plus

• Have designed or developed large data warehouses
• Understand the principles of data monitoring and quality assurance
• Are familiar with the Hadoop technology stack
• Have worked with the DataLens data visualization system or its analogues