Responsible AI Tech Lead

Responsible AI Tech Lead

Project Role : Responsible AI Tech Lead
Project Role Description : Ensure the ethical and responsible use of artificial intelligence (AI) technologies. Design and deploy Responsible AI solutions; align AI projects with ethical principles and regulatory requirements. Provide leadership, fosters cross-functional collaboration, and advocates for ethical AI adoption.
Must have skills : Data Modeling Techniques and Methodologies
Good to have skills : NA
Minimum 12 year(s) of experience is required
Educational Qualification : 15 years full time education

summary:

Seeking a forward-thinking professional with an AI-first mindset to design, develop, and deploy enterprise-grade solutions using Generative and Agentic AI frameworks that drive innovation, efficiency, and business transformation. Senior individual responsible for designing and maintaining Data Vault 2.0 models for a specific domain or subject area within a larger enterprise data platform. As a Data Modeler, you will engage with key business representatives, data owners, end users, application designers, and data architects to model both current and new data. You should have experience with profiling structured, semi-unstructured and non-relational data. You will also be responsible for ensuring that the data models are aligned with best practices and industry standards. This role requires a proactive approach to problem-solving and commitment to delivering high-quality data solutions that enhance decision-making processes.

Roles & Responsibilities:

- Lead AI-driven solution design and delivery by applying GenAI and Agentic AI to address complex business challenges, automate processes, and integrate intelligent
insights into enterprise workflows for measurable impact.
- Design and build Hubs, Links, and Satellites for assigned business domains, following established Data Vault 2.0 standards
- Translate business/source system requirements into conceptual, logical, and physical Data Vault models
- Perform source-to-target mapping and collaborate with ETL/ELT developers on load pattern implementation (batch and near-real-time)
- Handle multi-source integration challenges — conformance, business key standardization, same-as/hierarchy links
- Implement and maintain Point-in-Time (PIT) and Bridge tables for downstream consumption (Business Vault/Information Marts)Ensure historization, auditability, and
traceability requirements are met per data governance standards
- Drive automation strategy for DV pattern generation to reduce manual build effort at scale
- Participate in data model reviews and enforce naming/design conventions
- Document data lineage and modeling decisions

Professional & Technical Skills:
- Strong grasp of Generative and Agentic AI, prompt engineering, and AI evaluation frameworks. Ability to align AI capabilities with business objectives while ensuring
scalability, responsible use, and tangible value realization.
- Must To Have Skills: Strong hands-on experience with dimensional modeling (Kimball) as a foundation, plus demonstrated DV2.0 project delivery
- Proficiency in at least one cloud data platform (Snowflake, Redshift, Big Query, Synapse)
- Strong understanding of database design principles and data architecture.
- Experience with data integration tools and ETL processes.
- Familiarity with data governance and data quality frameworks.
- Ability to translate business requirements into technical specifications.
- Understanding of banking/financial services data domains preferred (regulatory reporting, transaction/account hierarchies)
- Exposure to automation tools for DV generation (Where Scape, VaultSpeed, biGENIUS) is a plus.

Additional Information:
- The candidate must have minimum 11 years of experience in Data Modeling Techniques and Methodologies, with at least 4–5 years dedicated to Data Vault 2.0
- A 15 year full time education is required.