Manager - Data Consulting & Design

Job Summary :

- Accenture Data & AI is seeking an experienced and client-focused Manager - Data Consulting and Design to join our Banking practice.

- This role will lead the design and delivery of enterprise-scale data modernization and data product initiatives, with a strong focus on data modeling, governance, and business-aligned architecture.

- The ideal candidate will bring deep expertise in conceptual, logical, and physical data modeling, along with strong banking domain knowledge, to build scalable data solutions for analytics, reporting, regulatory, and operational use cases.

Roles and Responsibilities :

- Act as a trusted data advisor to banking clients shaping Data Modernization roadmaps and Data Product strategies with clear business outcomes

- Design enterprise-scale data models (conceptual through physical) across analytics, reporting, regulatory, and operational use cases

- Lead the design and governance of shared data products defining data contracts, conformed dimensions, and reusable reference models

- Bridge the gap between business stakeholders and engineering teams, translating domain requirements into actionable model designs

- Lead and mentor a small POD of Business Analyst, Jr. Data Modeler and Data Scientists, ensuring quality delivery and knowledge sharing

- Strong Banking Domain expertise, preferably literacy in Core Banking platform

- Mentorship : Guide and Mentor junior team members, fostering a culture of innovation and continuous learning

Professional & Technical Skills :

- Minimum 6+ years of experience in Data Modernization program, Data Model Design and Data & AI

- Strong experience in building scalable data models using tools like Erwin/ER Studio

- Strong Banking Domain expertise, preferably knowledge of one or more in Core Banking platform/products

- Exposure to One or More cloud technologies (AZURE, AWS, Databricks, Snowflakes) is a plus but not a mandatory skill

- Ability to engage senior stakeholders, translate business requirements into data architecture decisions, and present modeling choices with clear business rationale