In this role you will:

• 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

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.

In this role you will:

• 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

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.