Finance Domain Data Quality Analyst

Overview

Mizuho – Enterprise Data Management Office

The Enterprise Data Management function is responsible for effective and consistent data management that is critical for external reporting, informing bank management, and protecting customers from financial loss or severe impact when data is deficient.

Mizuho’s data operating model comprises enterprise-wide groups and federated data domains to drive accountability and management of data. The federated model minimizes overlaps and reduces handoffs of data attributes across the data lifecycle. Federated data domains are defined by data types originated and consumed by the enterprise (transactional, derived, and master/reference) to prevent unclaimed or overlapping data elements between domains. Data is clustered into federated domains with ownership for data quality from origination to consumption.

Core responsibilities of Data Domains include defining and owning business use cases, managing data within the domain, ensuring data meets the needs of data consumers, guiding data quality assessments and remediation with source systems, modelling and defining data definitions, and participating in enterprise data governance bodies. Data Domains are supported by Data Stewards and Data Quality Analysts in the EDMO organization and act as a bridge between EDMO and the Data Domains.

The Finance Data Domain will manage data initiatives related to Mizuho’s Finance businesses.

Role

Title:

Finance Domain Data Quality Analyst

As a Finance Domain Data Quality Analyst, you will work directly with the Finance Data Steward as the driving force behind our end-to-end data strategy, serving as the subject matter expert for the Finance Data domain and supporting the Domain Sponsor, Data Consumers, Data Owners, and Data Architects. The primary function of the Data Stewards & Data Quality Analysts is to ensure the data assets of their domain are fit-for-use (analytical or operational) and fit-for-purpose.

You will collaborate with business teams, IT leads, and data consumers to create and execute a comprehensive data strategy and engage with business contacts to understand data requirements, data models and architecture, usage, and challenges to be addressed by the data strategy.

We are seeking a meticulous and analytical Finance Data Quality, Analytics, and Controls Analyst to join our management team. The successful candidate will be responsible for ensuring the quality and integrity of finance-related data, investigating authoritative data sources, conducting data analysis to support risk assessments, and implementing data control measures to safeguard critical data assets. This role requires strong data management skills, proficiency in data analysis tools, and a deep understanding of data management practices.

• Data Catalogue, Lineage and Analytics:
  Develop and maintain data catalog, list of pain points, dashboards, reports, and visualizations to provide insights to stakeholders.
• Drive data lineage to ensure sourcing from authorized systems.
• Maintain and identify changes needed in metadata for domain data.

• Data Analytics:
  Analyze large datasets to identify trends, patterns, and risk factors that may impact the organization.
• Perform ad-hoc analysis to answer specific needs.

• Data Quality:
  Develop and manage data quality controls for the domain to ensure accuracy, completeness, and consistency of data; identify trends and risk factors affecting stakeholders and the organization.
• Develop and maintain reporting to provide insights to stakeholders and identify/correct data discrepancies or anomalies.
• Collaborate with data owners and IT teams to establish data quality standards and metrics.
• Maintain documentation of data quality issues and corrective actions taken.

• Data Controls:
  Develop and manage data quality controls to satisfy consumer requirements; oversee remediation of data controls, participate in root cause analyses, prioritize criticality and remedial actions, and participate in audits of data control processes as needed; perform data quality checks and audits to identify and rectify discrepancies or anomalies; collaborate with data owners and IT teams…