Data Modeler

Key Skills: Data Modelling, SQL, Tools (Erwin OR ER/Studio)
Job Location: Hyderabad/Pune/Coimbatore/Bangalore
Mode of Work: Work From Office(5 Days)

Experience: 8-12 Years

About the job:
We’re seeking a hands‑on Data Modeler to design and evolve enterprise‑grade data models that make diverse source data consumption‑ready for reporting, analytics, and advanced insights. You will translate business requirements into conceptual, logical, and physical designs (3NF and dimensional), establish and enforce data standards and naming conventions, and collaborate across Architecture, BA, Engineering, and DBA teams to deliver performant, governed, and scalable models.

Know your team:

At ValueMomentum Technology Solution Centers, we are a team of passionate software engineering teams who thrive on tackling complex business challenges with innovative solutions catering to the P&C insurance value chain. We achieve this through a strong software engineering foundation and by continuously refining our processes, methodologies, tools, agile delivery teams, and core engineering archetypes. Our core expertise lies in six key areas: Platforms, Infra/Cloud, Application, Data, Core, and Quality Assurance.

Join our team that invests in your growth. Our Infinity Program empowers you to build your career with role-specific skill development, leveraging immersive learning platforms. You'll have the opportunity to work with some of the best minds serving insurance customers in the US, UK and Canadian markets.

Responsibilities:

• Model design & documentation: Create ERDs and data models (conceptual, logical, physical) for relational (3NF) and dimensional (star/snowflake) structures; maintain comprehensive documentation (data dictionaries, entity/attribute catalogs, glossaries).
• Multi source harmonization: Analyze and reverse engineer “as is” structures from varied source systems; design canonical and conformed models to unify and standardize data for enterprise use.
• Requirements translation: Partner with Information Architecture and Business Analysis to convert business concepts into precise technical designs, S2T (source to target) mappings, and DDL with clear integrity rules.
• Physical design alignment: Work with Data Architects and DBAs to validate physical design choices (partitioning, indexing, constraints) and ensure alignment to performance, reliability, and governance SLAs.
• Analytics & reporting enablement: Model aggregated and hierarchical structures and define fact/dimension layers, including SCD policies and conformed dimensions, to power BI, self service, and operational reporting.
• Standards & governance: Establish and enforce enterprise data standards (definitions, naming, keys, relationships), embed data quality and lineage considerations, and support stewardship and audit needs.
• Reference/master data: Define approaches to unify identifiers and entities across systems (golden record rules, survivorship, conformance strategies).
• Collaboration & leadership: Guide onshore/offshore contributors on modeling deliverables, review cycles, and documentation; facilitate stakeholder reviews and sign offs.
• Continuous improvement: Contribute to modeling standards, templates, and checklists; advocate reusable patterns across subject areas and lines of business.

Requirements:

• Strong data modelling expertise using ER diagrams and tools such as Erwin (or ER/Studio, equivalent).
• Proven delivery of conceptual, logical, and physical models at enterprise scale, spanning 3NF and dimensional approaches.
• Ability to gather and translate business requirements into clear technical designs, S2T mappings, and DDL (keys, constraints, indexes).
• SQL proficiency and deep understanding of database design, normalization, referential integrity, and performance considerations.
• Experience designing for aggregated & hierarchical reporting, SCD policies, and conformed dimensions.
• Effective collaboration with Architecture/BA/DBA/Engineering teams; excellent written and verbal communication skills.
• High attention to detail, consistency, and data quality across large and complex datasets.