Data Modeler

Wipro Technologies; Bengaluru; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Wipro Technologies is seeking a data modelling professional to map customer schemas to Industry Data Models (IDMs) across regulated verticals. You will define canonical patterns, document decision rationale, and curate golden datasets for AI agent training.

The role emphasizes governance, SQL proficiency, and experience with dbt and ETL tools. Join a team focused on enterprise data governance, semantic types, and cross-domain mappings to enable scalable, compliant data transformations in

Qualifications

• Experience in conceptual, logical, and physical data model design.
• Experience mapping customer schemas to canonical or reference data models.
• Proven ability to document mapping decisions and rationale for mappings.
• Experience with synthetic data generation and golden datasets.
• Strong SQL with column-level lineage tracing through transformations.
• Familiarity with industry reference models (FIBO, FHIR/OMOP, TM Forum, CDMC).

Responsibilities

• Analyze customer data estates across regulated verticals and map to Industry Data Models (IDMs).
• Define and maintain IDM mapping standards and canonical patterns.
• Annotate and validate AI data modelling outputs with clear reasoning.
• Build golden datasets for training and evaluation of AI agents.
• Design synthetic data sets preserving structure and statistics.
• Review IDM-aligned outputs for correctness and governance.

Skills

Data modelling

SQL proficiency

Governance

dbt / transformation tools

Graph data models

Documentation / communication

Education

Bachelor's or Master's degree in Computer Science or related field

Tools

dbt

ETL tools

Job description

• Analyze customer data estates across Financial Services, Healthcare, Telecommunications, and other regulated verticals, and produce authoritative source-to-target mappings from customer physical schemas onto Industry Data Models (IDMs) — covering entities, attributes, keys, grain definitions, and business logic transformations.


• Define and maintain IDM mapping standards, canonical equivalence patterns, and naming conventions for each supported industry vertical — the governing artefacts that AI mapping agents use at runtime to propose and validate mappings autonomously.


• Define and provide behavioral ground truth for the AI data modelling agent: annotate correct mappings, flag incorrect proposals, and document the reasoning behind every accepted or rejected agent decision — forming the authoritative reference the agent is trained and evaluated against.


• Build and curate high-quality golden datasets for agent training and evaluation — multi-industry, multi-domain mapping examples spanning clean cases, edge cases, ambiguous entities, cross-system synonyms, and known failure modes.


• Design and generate synthetic data sets that faithfully reproduce the structural and statistical properties of real customer schemas without exposing customer data — enabling safe, scalable agent training, regression testing, and evaluation suite expansion.


• Validate AI-generated outputs: review auto-proposed data models, source-to-target mappings, and dbt/SQL transformation artefacts for correctness, completeness, and adherence to IDM and governance standards.


• Define, author, and govern the Semantic Data Type vocabulary — connecting physical columns to governed business concepts and data quality expectations across all supported IDM verticals.


• Author and maintain the Business Glossary for each industry domain: terms, definitions, synonyms, hierarchies, and relationships; drive import of industry-standard glossaries (FIBO for Financial Services, FHIR/OMOP for Healthcare, TM Forum for Telecommunications, CDMC cross-vertical).


• Review and curate semantic type assignments produced by the platform's automated tagging and classification pipeline; act as the authoritative steward for the controlled Semantic Data Type vocabulary.


• Collaborate with Graph Engineers on Context Graph schema design — ensuring the graph model encodes IDM entity relationships, equivalence groups, and cross-industry concept alignments in a form traversable by AI agents at inference time.


• Work with Data Quality engineers to ensure IDM-aligned Semantic Data Types are correctly linked to DQ expectations, validation rule sets, and SLA categories for each vertical.


• Produce and maintain enterprise data modelling guiding principles and naming standards consumed by AI agents at runtime to generate consistent, governed, industry-aligned artefacts.

Who You’ll Work With

• Are part of Teradata’s global engineering organization, responsible for building the technologies that power VantageCloud, our unified data and AI platform.


• Operate at the intersection of cloud computing, advanced analytics, and AI-driven automation to help enterprises unify and analyze data across hybrid and multi-cloud environments.


• Solve highly complex challenges in scalability, performance, interoperability, and intelligent automation to enable customers to turn data into insights and innovation.


• Collaborate across research, architecture, platform engineering, and product teams to shape the future of enterprise AI.


• This position reports into the AI engineering leadership team within Teradata’s global engineering organization.

On our team, we:

• Are part of Teradata’s global engineering organization, responsible for building the technologies that power VantageCloud, our unified data and AI platform.


• Operate at the intersection of cloud computing, advanced analytics, and AI-driven automation to help enterprises unify and analyze data across hybrid and multi-cloud environments.


• Solve highly complex challenges in scalability, performance, interoperability, and intelligent automation to enable customers to turn data into insights and innovation.


• Collaborate across research, architecture, platform engineering, and product teams to shape the future of enterprise AI.


• This position reports into the AI engineering leadership team within Teradata’s global engineering organization.

What Makes You a Qualified Candidate

• Proven experience in data modelling: conceptual, logical, and physical model design across relational and dimensional paradigms.


• Hands‑on experience with one or more industry canonical data models: FIBO (Financial Services), FHIR or OMOP (Healthcare), TM Forum (Telecommunications), CDMC, or equivalent.


• Experience mapping customer physical schemas onto canonical or reference data models — including multi‑hop source-to-target mappings, grain alignment, and business logic documentation.


• Ability to validate AI/LLM-generated data modelling outputs and articulate precisely why a proposed mapping is correct, incorrect, or ambiguous.


• Experience building evaluation or golden datasets: sampling strategies, edge case coverage, annotation workflows, and inter‑annotator agreement.


• Experience with synthetic data generation techniques for structured/tabular data.


• Strong SQL proficiency — able to trace column-level lineage through multi‑hop transformations, stored procedures, and views.


• Experience with data governance frameworks: data stewardship, DQ rule design, metadata lifecycle management.

What You’ll Bring

• Bachelor's or Master's degree in Computer Science, Information Systems, or a related field.


• 4–7+ years of experience in data modelling, data architecture, or enterprise data management.


• Deep expertise in at least one industry reference model framework (FIBO, FHIR, OMOP, TM Forum SID, CDMC, or equivalent) — with the ability to apply it to real customer schemas.


• Hands‑on experience producing source-to-target mapping specifications consumed by transformation tools (dbt, ETL, or AI agents).


• Experience authoring and governing business glossaries or ontologies across industry domains — in formal tools or platform-native environments.


• Experience designing golden datasets and evaluation corpora for AI or rules-based mapping systems; familiarity with annotation tooling and evaluation metric design.


• Experience with synthetic data generation for structured schemas — statistical fidelity, referential integrity preservation, and privacy-safe techniques.


• Advanced SQL skills: complex analytical queries, column-level lineage tracing through multi‑hop transformations, stored procedures, and UDFs.


• Familiarity with dbt for transformation modelling and dataset management.


• Strong written communication skills — able to document mapping decisions and modelling rationale for both technical engineers and business domain stakeholders.


• Familiarity with graph data models or semantic web standards (RDF, OWL, SKOS) is a plus.

Any complaints or concerns regarding unethical/unfair hiring practices should be directed to our Ombuds Group [e-mail] .

We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, caste, creed, religion, gender, marital status, age, ethnic and national origin, gender identity, gender expression, sexual orientation, political orientation, disability status, protected veteran status, or any other characteristic protected by law.

Wipro is committed to creating an accessible, supportive, and inclusive workplace. Reasonable accommodation will be provided to all applicants including persons with disabilities, throughout the recruitment and selection process. Accommodations must be communicated in advance of the application, where possible, and will be reviewed on an individual basis. Wipro provides equal opportunities to all and values diversity.