Sheris Dhanani

• Collaborated with stakeholders to define business requirements for master data management initiatives.
• Developed processes for standardizing, cleansing, and managing data using Informatica solutions.
• Mentored and managed consultants by coaching, delegating tasks, and reviewing work for quality assurance.
• Conducted deep-dive analyses on raw data to ensure accurate merging of master data using expertise in iterative matching techniques.
• Acquired certifications in MDM SaaS, CDQ, CDI, demonstrating commitment to continuous learning.
• Engaged with clients across health care, food, audit, and insurance sectors to deliver tailored solutions.

Configured the MDM Hub for the Deloitte Audit and Assurance department, with a goal to decrease the overall cost of delivering audits by reducing the time and effort required to acquire consistent and high-quality information about clients and engagements.

Conducted meetings with data stewards and architects for designing business rules on data validation.

Created batch groups for loading data into lookup base objects and all base objects.

Developed test suites and cases in SOAP UI for executing clean table and delete API functions.

Participated in bi-weekly Sprint planning and retrospective sessions to facilitate team improvements.

Established one-step approval processes using Active VOS workflow. Prepared data for Business Entity services using queries and packages for exposure in E360 UI.

Partnered with business teams to develop a centralized Customer Master data model from diverse source systems.

Developed system trust and match/merge strategies for various scenarios utilizing Exact and Fuzzy matching techniques.

Created and tested cleanTable, Delete, SearchQuery, getSearchResults APIs, and custom Java APIs via SOAP UI.

Collaborated with client-side data designers and architects to gather requirements such as Trust score and decay mechanisms.

Wrote optimized SQL procedures and queries to perform data cleansing efficiently.

Prepared data design and process flow documents to support project objectives.

Profiled data using custom facets and de-duplication processes, achieving an 80% data consistency improvement.