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.
Don't want to miss the next one?
Subscribe to daily email alerts for roles matching your interests.
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.
Role Purpose
The purpose of this role is to perform coding and unit testing as per the defined standards while coordinating with internal teams to ensure outputs align with client requirements.
Areas of responsibility Gathering of requirements-Assist in creating documentation of client requirements.
Coding and Configuration-"Perform coding and configuration activities in adherence to the standards of quality and delivery SLA. "
Testing and Trial Runs-"Perform unit testing, identify and report issues and assist in User Acceptance Testing (UAT)." Maintain Documentation-Maintain project documentation, update knowledge repositories, and contribute to standard operating procedures.
Stakeholder Collaboration-"Work with different teams to understand project needs, assist in development tasks and ensure alignment of output with client requirements."
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.
Similar roles you might like
More openings like this one — take a look before you go.
