Finanční analytici a špecialisti v oblasti finančníctva, poisťovníctva a ostatných finančných služieb

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Skills: - Proficient Excel skills for data analysis, reconciliation, and validation.    - Experience with data investigation, troubleshooting, and root cause analysis.    - Ability to understand business processes and translate them into structured solutions.    - Experience working with Neo4j or graph databases.    - Basic SQL knowledge, including the ability to read and understand queries.    - Background in insurance, financial services, or data operations.    - Proactive mindset with strong attention to detail and the ability to detect inconsistencies in data and processes.  - Experience collaborating with software developers and technical teams.    - Experience with data quality management or data governance.    - Knowledge of data analysis tools such as Power BI and Jira is a plus. Responsibilities: -Perform data curation and validation activities to ensure high data quality across core insurance datasets.    - Investigate and resolve complex data issues through deep analysis of data structures and system behaviour.    - Work with graph databases (Neo4j) and relational data structures to identify inconsistencies and anomalies.    - Conduct root cause analysis for data issues and collaborate with developers to implement sustainable fixes.    - Define and document business and technical requirements for improvements to data processes and systems.    - Support testing and validation of new technical solutions and enhancements before production deployment.    - Monitor data flows and business logic to ensure consistency across systems.    - Identify opportunities to improve data quality, processes, and automation.    - Deliver clear end-to-end outcomes when assigned tasks, including analysis, solution proposal, and final validation.    - Communicate findings and recommendations clearly to both technical and non-technical stakeholders.    - DQ issues management - Drive cross-functional squads (including MI, Systems, Data, and Operations) to resolve Data Quality issues.

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