
Data Quality Engineer
- București
- Permanent
- Full-time
- Data Quality Framework: Create and maintain Data Quality framework in alignment with Data Governance framework. Support initiatives related to master and reference data management, collaborating with business to identify, document, measure and improve data quality for critical data.
- Data Quality Assurance: Develop and implement data quality assurance solutions to monitor, measure, and improve data quality across NN.
- Data Profiling: Utilize data profiling techniques to analyze data quality and integrity, proving valuable insights for decision making and remediation efforts.
- Data Governance Tools:
- Set up and maintain Collibra data dictionary and reporting, including data lineage and integration with business terms glossary and data quality business rules. Implement and maintain Data Discovery, that involves identifying and categorizing data assets such as databases, files, applications, and other data sources, and understanding the relationships between them, in Collibra.
- Develop and maintain Data Quality processes in Talend: DQ rules in Data Stewardship; ETL workflows and orchestrators in Talend Studio; DQ rules exploration in Data Preparation; Change Data Capture incremental data collection mechanism from source systems to Talend platform; reports & dashboards on data correctness and completeness (xls, PowerBI).
- Data Structures and Relationships: Understand the principles and support in defining standards that ensure a unitary documentation of the types of data used and stored within the system, the relationships among these data types, the ways the data can be grouped and organized and its formats and attributes.
- Data Integration: Implement and maintain data integration workflows, including ETL processes needed for data quality checks. Optimize ETL processes for performance and scalability. Develop and maintain documentation related to ETL processes. Troubleshoot and resolve ETL-related issues. Integrate Collibra with Talend and both with source systems and consuming systems. For both Collibra and Talend, ensure autonomous data ingestion from source systems.
- Data Warehousing, Data Lake, Business Intelligence: Understanding the principles in order to identify and propose for implementation solutions for improving data processes with the aim to reduce manual effort, quality issues and to increase the access to data by facilitating data retrieving and analyzing that provide useful insights and support management decisions. Collaborate with Data Architect and Product Owners for identifying the best solutions.
- Cross-Functional Collaboration: Collaborate with IT and business stakeholders to implement data governance solutions effectively.
- Training and Support: Creates and delivers related trainings within company to increase data quality acknowledgement and to ensure ongoing data quality management according to Data Governance framework.