We are the CORE analytics team at the CX headquarters, building work with customer characteristics and actions data for the bank.
The key role of this position is to create and maintain a data layer (raw data → aggregated → metrics layer) for data analysts and to automate data delivery to BI.
We are looking for someone who: has written scripts for calculating metrics, procedures, and data update pipelines in SQL+Python — and at the same time has a good understanding of the business meaning of what is being calculated.
Responsibilities
- design and development of the analytical layer: DWH architecture, data models for Greenplum and Hadoop, development and support of SQL scripts for calculating product and customer metrics, creating new "turnkey" metric calculations
- automation and orchestration: Airflow DAG development, pipeline optimization, automation of routine ETL operations (transformations, loads, cleaning, reports)
- data quality and validation: implementation of DQ checks (anomalies, nulls, types, logical inconsistencies), building quality monitoring
- support, scaling, and documentation: ETL and data mart support, system expansion for new sources/metrics, documentation of architecture, transformations, and business rules
- analytical component: understanding the meaning of metrics, searching for insights, customer analytics (collection, grouping, conclusions, documentation), collaboration with the BI team
- knowledge sharing: transferring knowledge to adjacent analysts, developing best practices for the team.
Requirements
- advanced SQL: experience in writing large complex scripts for calculating metrics, procedures, and data update pipelines in SQL; confident query optimization and working with large volumes (window functions, CTE, complex joins, aggregations)
- experience in building ETL/ELT pipelines for 2+ years
- experience with Airflow or analogues
- confident Python (pandas, numpy, generators, OOP)
- experience with OLAP storage (Greenplum, ClickHouse, Vertica, etc.) and Hadoop ecosystem (Spark, Hive, HDFS), understanding spark configurations
- understanding of DWH concepts (data marts, dimensions, facts, normalization)
- basic knowledge of DQ checks and data validation
- Git proficiency
- skills in working with generative AI models; experience in creating AI agents and using them in work will be an advantage
- experience using GigaChat, Kandinsky, and analogues in products, skills
- statistics and mathematics: hypothesis testing, normal distribution, basic statistical tests
- A/B testing: theory, interpretation of results, design
- customer analytics: segmentation, user personas, cohort analysis, retention/churn calculation
- ability to find insights in data and explain them to the business.
Conditions
- comfortable modern office near Kutuzovskaya metro station
- work format - office
- annual salary review, annual bonus
- corporate gym and rest areas
- over 400 educational programs at SberUniversity for professional and career development
- extended voluntary medical insurance, preferential insurance for family, and corporate pension program
- flexible mortgage discount, equal to 1/3 of the Central Bank's key rate
- free SberPrime+ subscription, discounts on partner company products
- reward for recommending friends to the Sber team.
