We are a team that develops an analytical framework for management risk reporting: we build data marts, analyze the credit portfolio and risk metrics, automate reporting using BI tools, and develop an AI agent for working with analytical requests. The team works at the intersection of risk analytics, data, and internal AI tools.
Tech Stack:
SQL, Python, pandas, PySpark, Apache Spark, Hadoop/HDFS, BI tools, PostgreSQL, GreenPlum, ClickHouse.
Responsibilities
- Gathering and analyzing requirements, developing prototypes of analytical data marts;
- Designing the architecture of data loading flows and data model design;
- Working with data using SQL, Python, PySpark, and DBMS: GreenPlum, Hadoop, ClickHouse, PostgreSQL;
- Developing and supporting BI reporting throughout the full cycle, conducting demonstrations for clients;
- Actively interacting with analysts/data engineers from other teams within Agile processes;
- Verifying data quality, correctness of calculations, analytical hypotheses, and AI agent responses;
Requirements
- At least 2 years of experience as a data analyst/DWH/BI analyst;
- Ability to describe the business logic of calculations and verify the correctness of calculations, data, and analytical conclusions;
- Excellent SQL knowledge (any dialect): complex queries, analytical functions, understanding of join physical implementation, query performance optimization;
- Experience with one or more DBMS: PostgreSQL, Green Plum, ClickHouse, Hive. Understanding the principles of MPP systems;
- Understanding the principles of data warehouse organization, approaches to designing logical and physical models, and data mart design;
- Analytical mindset, ability to work independently, possessing skills and a drive to quickly find solutions to technical and algorithmic problems, involving experts when necessary;
- Strong communication skills, ability to negotiate and clearly articulate thoughts, willingness and ability to work with people;
- Ability to apply modern data analysis and machine learning tools (ChatGPT/DeepSeek/GigaChat) to improve work efficiency;
Will be a plus:
- Knowledge of banking subject area;
- Experience with PySpark / Apache Spark: DataFrame API, aggregations, join, partitioning, working with parquet data;
- Python knowledge, experience in data processing and analysis using specialized libraries such as pandas and matplotlib;
- Skills in working with generative AI models;
- Experience in creating AI agents and using them in work;
- Knowledge of statistics, principles of mathematical modeling, and hypothesis testing.
Conditions
- Comfortable modern office near Kutuzovskaya metro station
- Office-based work format
- Annual salary review
- Corporate gym and recreation areas
- Over 400 educational programs from SberUniversity for professional and career development
- Adaptation program and manager support at the start (for Junior positions)
- Expanded voluntary medical insurance, preferential insurance for family, and corporate pension program
- Mortgages at an advantageous rate of up to 7% for every employee
- Free SberPrime+ subscription, discounts on partner company products
- Reward for recommending friends to the Sber team.
