- Employment: full-time
- Location: Russian Federation
- Citizenship: Russian Federation
- Working hours: Moscow time ±2 hours
- Format: Remote
Position
- Role: Data Analyst
- Level: Middle+, Middle
Must-have
- Experience with data quality: understanding of key DQ criteria, experience in formalizing data quality rules and checks, as well as data profiling — analyzing structure and content, identifying missing values, duplicates, anomalies, and inconsistent values.
- Experience with DWH/Data Warehouse: understanding of warehouse structure, data sources and flows, ability to analyze discrepancies and incorrect data.
- Experience interacting with data owners, analysts, and source system teams: gathering and formalizing requirements, agreeing on data quality rules and checks.
- Proficient in SQL: complex JOINs, subqueries, window functions; using SQL for data analysis, profiling, and validation; understanding basic query optimization principles.
- Understanding of ETL/ELT processes and data loading and transformation principles.
- Practical experience with Oracle and/or PostgreSQL.
- Experience analyzing data quality incidents: problem localization, error source identification, interaction with responsible teams.
- Basic understanding of Apache Airflow: DAGs, tasks, dependencies, schedules; willingness to work with typical DAGs for DQ checks.
Nice-to-have
- Experience with Data Catalog / metadata, glossaries, and data descriptions.
- Experience in the banking domain, especially with credit data, credit bureaus (BKI), or regulatory reporting.
- Experience with Informatica ETL or other ETL tools.
- Experience with Service Desk / incident management systems.
- Experience with FineBI or similar BI tools.
- Basic Python skills.
- Knowledge of PD, LGD, EAD, and PVr — as an additional advantage.
Tasks
- Development, configuration, and maintenance of data quality checks.
- Data analysis and profiling: identifying discrepancies, missing values, duplicates, anomalies, and other quality issues.
- Investigating data quality incidents: localizing the problem source, identifying the responsible team, and supporting the resolution.
- Interaction with data owners, analysts, and source system teams: gathering and formalizing data requirements, agreeing on quality rules and checks.
- Working with DWH/Data Warehouse: analyzing sources, loading flows, and data transformations.
- Preparing requirements for enhancing data quality control tools and processes.
- Working with DQ checks implemented on Apache Airflow: configuration, execution, monitoring, and result analysis.
- Working with the data catalog and metadata: updating data descriptions, attributes, relationships, and glossaries together with data owners.
- Participation in the development of Data Governance processes and data quality methodology.
- Configuration and maintenance of reconciliations between systems and data sources.
- Participation in the formation and development of data delivery SLAs.
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