Data Risk EngineerSenior Associate

Industry/Sector

Specialism

Management Level

Job Description & Summary

Focused on relationships you are building meaningful client connections and learning how to manage and inspire others. Navigating increasingly complex situations you are growing your personal brand deepening technical expertise and awareness of your strengths. You are expected to anticipate the needs of your teams and clients and to deliver quality. Embracing increased ambiguity you are comfortable when the path forward isnt clear you ask questions and you use these moments as opportunities to grow.

Examples of the skills knowledge and experiences you need to lead and deliver value at this level include but are not limited to:

• Respond effectively to the diverse perspectives needs and feelings of others.
• Use a broad range of tools methodologies and techniques to generate new ideas and solve problems.
• Use critical thinking to break down complex concepts.
• Understand the broader objectives of your project or role and how your work fits into the overall strategy.
• Develop a deeper understanding of the business context and how it is changing.
• Use reflection to develop self awareness enhance strengths and address development areas.
• Interpret data to inform insights and recommendations.
• Uphold and reinforce professional and technical standards (e.g. refer to specific PwC tax and audit guidance) the Firms code of conduct and independence requirements.

Minimum years experience required

4 Years-8 Years

This role focuses on designing developing and maintaining metadata processing data lineage and data quality solutions using various tools to support organizational data management policies. Responsibilities include implementing data integration and quality processes resolving data issues through root cause analysis documenting data models and collaborating with business and IT stakeholders to ensure compliance with data governance frameworks and improve data integrity and security.

Roles and Responsibilities

• Design develop and maintain metadata processing solutions using tools such as Ab Initio MetaHub Informatica EDC Alation Collibra and OvalEdge software to support the clients data management policies and standards. Collaborate with business analysts and data architects to understand requirements.
• Develop and implement data integration solutions for metadata data lineage and data quality.
• Develop implement and maintain data quality processes to ensure data integrity across the organization by monitoring data quality metrics performing regular data quality assessments and collaborating with data owners and stewards to ensure compliance with data quality standards.
  • Identify document and manage data quality issues to ensure timely and effective resolution while leading root cause analysis and implementing corrective actions to prevent recurrence of these issues.
• Develop implement and maintain data quality processes to ensure data integrity across the organization by utilizing data quality tools to develop and manage data quality rules and processes.
• Develop custom data lineage solutions for complex technology platforms.
• Design enhance and sustain metadata and data pipelines to facilitate periodic data quality monitoring within the clients data management system.
• Conduct data analysis to identify gather and transfer data across various systems integrating and linking diverse data sets to summarize and present insights and recommendations in the most suitable format for stakeholders.
• Document data models at physical logical and conceptual levels to support bank-wide IT and analytics issue identification and development.
• Analyze document and resolve issues identified from data mapping and data conversions and manage stakeholder engagement to resolve them with both business and IT SMEs.
• Partner with IT and business to design and implement data lineage and Data quality solutions that adhere to the Clients data principles and governance framework.
• Partner with business to translate business criteria into technical data quality rules.
• Support and influence the definition of clear goals to improve data collaboration data lineage data quality and reduce data architecture complexity and information security risks.

Travel Requirements

Job Posting End Date