We are developing an analytical environment for risk management reporting: building data marts, analyzing the credit portfolio and risk metrics, automating reporting using BI tools, and developing an AI agent for analytical queries. 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 / ClickHouse.

Responsibilities

• Developing dashboards "from scratch" in the "Navigator" BI system (internal bank solution);

• Interacting with counterparties, analyzing and forming report requirements;

• Developing analytical marts for the credit portfolio and risk metrics;

• Working with data using SQL, Python, and PySpark;

• Verifying data quality, calculation correctness, analytical hypotheses, and AI agent responses;

• Participating in the development of an AI agent for reporting and analytical queries;

• Preparing analytical conclusions for the business and interacting with business stakeholders, developers, and data owners.

Requirements

• At least 2 years of experience as a data analyst in banking, fintech, risk, product, or portfolio analytics;

• Experience developing dashboards in Power BI (DAX, Power Query) / Tableau / DataLens;

• Knowledge of BI development cycles: Understanding ETL processes, principles of working with data warehouses (DWH);

• Experience developing analytical marts or regular data reporting layers;

• Proficient SQL: complex queries, joins, CTEs, window functions, aggregations, optimization of heavy queries;

• Python for data analysis: pandas, numpy, processing large tables, automating calculations and checks;

• Experience with PySpark / Apache Spark: DataFrame API, aggregations, joins, partitioning, working with parquet data;

• Ability to describe business logic of calculations and verify data correctness;

• Ability to verify the correctness of calculations, data, and analytical conclusions;

• Excellent communication skills, ability to work with cross-functional teams and convey complex ideas to non-technical specialists.

Will be a plus:

• Interest in LLMs, text2sql, and AI agents;

• Experience with Hadoop/HDFS, Hive, or other Big Data tools;

• Knowledge of statistics, principles of mathematical modeling, and hypothesis testing;

• Experience automating analytical reporting in Python;

• Experience with PostgreSQL / ClickHouse or other MPP/Big Data warehouses.

Conditions

• comfortable modern office: Kutuzovsky Prospekt 32
• office work format
• annual salary review and annual bonus
• corporate gym and recreation areas
• over 400 educational programs from SberUniversity for professional and career development
• enhanced 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 products from partner companies
• reward for recommending friends to the Sber team.