☁️Position: Data Quality Analyst (Middle+)
🏙Company: «HuntTech»
💪Experience: from 4 years
💰Salary: Range: 209 - 258k RUB
📅Work format: Remote
📍Location/Citizenship: RF
📅Employment: Full-time
☎️Contact: @pavel_korab
📌Responsibilities:
- Development, setup, and maintenance of data quality checks.
- Data analysis and profiling: identification of discrepancies, missing values, duplicates, anomalies, and other quality issues.
- Analysis of data quality incidents: localization of the problem source, identification of the responsible team, and support for resolution.
- Interaction with data owners, analysts, and source system teams: collection and formalization of data requirements, agreement on quality rules and checks.
- Work with DWH/CDH: analysis of sources, loading flows, and data transformations.
- Preparation of requirements for refining data quality control tools and processes.
- Work with DQ checks implemented on the basis of Apache Airflow: setup, launch, execution monitoring, and results analysis.
- Work with the data catalog and metadata: updating data descriptions, attributes, relationships, and glossaries jointly with data owners.
- Participation in the development of Data Governance processes and data quality methodology.
- Setup and maintenance of reconciliations between systems and data sources.
- Participation in the formation and development of SLAs for data delivery.
🔺Requirements:
- Experience with data quality: understanding of basic DQ criteria, experience in formalizing data quality rules and checks, as well as data profiling — analysis of structure and content, identification of missing values, duplicates, anomalies, and inconsistent values.
- Experience with DWH/CDH: understanding of the storage structure, sources, and data flows, ability to analyze the causes of discrepancies and incorrect data.
- Experience interacting with data owners, analysts, and source system teams: collection and formalization of requirements, agreement on data quality rules and checks.
- Proficient in SQL: complex JOINs, subqueries, window functions; using SQL for analysis, profiling, and data 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, scheduling; willingness to work with standard DAGs for DQ checks.
➕Will be a plus:
- 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 VRF — as an additional advantage.
🔥If you like everything - write to @pavel_korab
