Modernization Engineer

Modernization Engineer

Project Role : Modernization Engineer
Project Role Description : Build and test agile and cost effective hosting solutions. Implement scalable, high performance hosting solutions that meet the need of todays corporate and digital applications using both private and public cloud technologies. Develop and deliver legacy infrastructure transformation and migration to drive next-generation business outcomes.
Must have skills : Data Modeling Techniques and Methodologies
Good to have skills : NA
Minimum 15 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. The person should be able to own the enterprise Data Vault architecture across multiple domains or an entire program. Should be able to set modeling standards, arbitrate cross-domain design conflicts, and is the technical authority client stakeholders and delivery teams escalate to. This would be a mix of architecture-and-governance role along with hands-on table design.
Roles & Responsibilities:
- Define and own the enterprise Data Vault 2.0 modeling standards, naming conventions, and governance framework across the program
- Architect the Raw Vault / Business Vault / Information Mart layering strategy for the full platform, not a single domain
- Make and defend cross-domain design tradeoffs (e.g., same-as links vs. hierarchical links, satellite splitting strategy, driving keys for multi-active satellites)
- Own capacity/performance architecture decisions — clustering keys, micro-partitioning strategy (Snowflake-specific), load parallelization
- Lead technical reviews and sign off on modeling decisions made by senior modelers
- Interface directly with client architects, data governance leads, and executive stakeholders on modeling strategy
- Mentor and technically direct a team of 3–8 senior/mid-level modelers
- Own the data model roadmap in alignment with migration/modernization program milestones (e.g., legacy-to-cloud lineage, COBOL/mainframe source integration)
- Drive automation strategy for DV pattern generation to reduce manual build effort at scale

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: Proven track record owning DV architecture for at least one large, multi-year program (not just project delivery)
- Deep expertise in Snowflake (or equivalent) internals relevant to DV performance — this needs to be genuine architecture depth, not tool familiarity
- Strong stakeholder management experience — this role is client-facing by design
- Experience integrating legacy/mainframe sources (COBOL, JCL, copybooks) into modern DV models is a strong differentiator, not just a nice-to-have, if this is for a
banking modernization context
- Experience with regulatory/compliance-heavy domains (banking, insurance) strongly preferred
- Exposure to automation tools for DV generation (Where Scape, Vault Speed, biG ENIUS) is a plus.

Additional Information:
- The candidate must have minimum 14 years of experience in Data Modeling Techniques and Methodologies, with 6+ years specifically architecting (not just building) Data Vault 2.0 models at enterprise scale
- A 15 year full-time education is required.