Finance Data Management and Governance - Senior - Tech Consulting - Location Open
EYAbout the role
Location: Chicago, Akron, Arlington, Atlanta, Austin, Baltimore, Birmingham, Boca Raton, Boston, Buffalo, Charleston, Charlotte, Chattanooga, Chicago, Cincinnati, Cleveland, Columbia, Columbus, Dallas, Denver, Des Moines, Detroit, Edison, Fort Worth, Grand Rapids, Greenville, Hartford, Hoboken, Honolulu, Houston, Indianapolis, Irvine, Jacksonville, Kansas City, LA, Las Vegas, Louisville, McLean, Memphis, Miami, Milwaukee, Minneapolis, Nashville, New Orland, New York, Oklahoma, Orlando, Palo Alto, Philadelphia, Phoenix, Pittsburgh, Pleasanton, Portland, Providence, Raleigh, Richmond, Rochester, Rogers, Sacramento, Salt Lake City, San Antonio, San Diego, San Francisco, San Jose, Seattle, Secaucus, Stamford, St. Louis, Syracuse, Tallahassee, Tampa, Toledo, Tucson, Tulsa, Washington DC, Westlake village, Winston-Salem
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Finance Data Management and Governance, Senior, Tech Consulting
Whole industries have been disrupted and transformed in recent years by changing technologies, advanced analytics and the need for better insight. EY is helping businesses realize the value they can gain from their IT investments. We deliver exceptional client service — providing advice on how technology, finance process efficiency and enterprise intelligence contribute to performance improvement, as well as how IT can act as a multiplying effect during major program transformations.
The opportunity
The Finance Applications Data Manager is a crucial role responsible for supporting the Finance Applications Data Lead in executing the overall data management strategy for finance applications. The successful candidate will leverage their deep expertise finance applications (planning, reporting, close/consolidation) coupled with deep skills in enterprise data management, data governance, data quality, master data management, Machine Learning, and Generative AI (Gen AI) to support key finance personas. One of the key responsibilities will focus on developing and implementing our “FDL consulting blueprint” service offering, with the goal of creating an industry agnostic data model which can be utilized as a starting point and be extended ensure data consistency and interoperability across finance applications. The successful candidate will work closely with the Data Lead and the Product Owner for the FDL to ensure that the FDL Blueprint is designed on a foundation of accurate, consistent, and reliable finance application data architecture, enabling informed decision-making.
Your key responsibilities
The Finance Applications Data Manager will work closely with finance, IT, and data science teams to support the effective management and utilization of finance application data, harnessing the power of Machine Learning, Gen AI, and Azure data technologies to drive innovation and business value through the development and implementation of the Azure Finance Lakehouse solution offering.
Skills and attributes for success
- Fostering relationships with client personnel at appropriate levels. Consistently running and delivering quality client services. Driving high-quality work products within expected time frames and on budget.
- Monitoring progress, managing risk and confirming that key stakeholders are kept informed about progress and expected outcomes.
- Managing expectations of client service delivery.
- Effectively managing and motivating client engagement teams with diverse skills and backgrounds. Providing constructive on- the- job feedback/coaching to team members.
- Fostering an innovative and inclusive team-oriented work environment. Playing an active role in the counselling and mentoring of junior consultants within the organization.
- Supporting Data Management Strategy Execution, including helping execute the overall data management strategy for finance applications
- Collaborating with cross-service line teams, including Finance, Managed Services, and Tech Consulting to ensure alignment and integration of finance application data with related data initiatives
- Defining data requirements, data architecture, and data models for finance applications, considering the potential of Machine Learning and Gen AI technologies
- Leading the design and implementation of an extensible common info
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