Ey – GDS Consulting - AI and DATA-Semantic Data Architect-Manager
No salary listed
Expert
Hyderabad, Telangana, India
In Person
Data & AnalyticsData Management
About the job
Requirements
- At least 10 years of experience in data architecture, data engineering, enterprise information management, or enterprise data modelling-led architecture roles.
- At least 5 years of hands-on experience in semantic technologies, knowledge modelling, and enterprise data modelling.
- Hands-on experience with Microsoft Fabric, semantic models, the Power BI semantic layer, conceptual, logical, and physical data modelling, RDF/OWL ontology engineering, knowledge graph platforms, and governance platforms such as Microsoft Purview or Collibra.
Responsibilities
- Develop domain ontologies using RDF/OWL, taxonomies, controlled vocabularies, SKOS concept schemes, and semantic models to standardize business knowledge representation.
- Collaborate with business analysts, data architects, domain subject-matter experts, and stakeholders to elicit business concepts, align semantic definitions, and translate requirements into ontology-driven models.
- Establish semantic governance processes, metadata standards, ontology lifecycle management, and reusable semantic modelling patterns.
- Define semantic standards that connect business glossaries, domain concepts, enterprise data assets, and AI-ready knowledge structures.
- Lead conceptual, logical, and physical data modelling across enterprise domains, ensuring data entities, relationships, hierarchies, cardinality, keys, constraints, and business definitions are clearly captured and governed.
- Create and maintain enterprise, canonical, domain, dimensional, and semantic data models that align business terminology with analytical, lakehouse, relational, and graph-based data structures.
- Translate business requirements into information models and data structures that support Microsoft Fabric Semantic Models, Power BI semantic layers, knowledge graphs, GraphRAG, enterprise search, and AI-ready data products.
- Apply modelling disciplines including normalization, denormalization, star and snowflake schema design, slowly changing dimensions, reference and master data modelling, metadata modelling, and model version management.
- Partner with business subject-matter experts, architecture teams, data stewards, and engineering teams to ensure data models are reusable, governed, explainable, lineage-aware, and aligned with enterprise data governance standards.
- Design and implement enterprise knowledge graph solutions that model business entities, relationships, hierarchies, dependencies, and contextual knowledge.
- Build semantic layers that support AI, intelligent search, recommendations, GraphRAG architectures, and contextual knowledge retrieval.
- Integrate structured, semi-structured, and unstructured data into enterprise knowledge graphs.
- Guide graph data modelling patterns for business domains, relationships, hierarchies, dependencies, and reusable knowledge assets.
- Design enterprise semantic architectures using Microsoft Fabric as the operational platform for data integration, governance, analytics, and AI-driven business insights.
- Architect Lakehouse, Data Warehouse, OneLake, and Semantic Models within the Microsoft Fabric ecosystem.
- Establish metadata-driven and domain-oriented data architecture patterns that connect lakehouse data products, semantic layers, AI retrieval, and governance workflows.
- Partner with data engineering teams to operationalize semantic assets through reliable, governed, and scalable data platform patterns.
- Enable AI and generative AI solutions through semantic enrichment, contextualized data models, and ontology-driven knowledge assets.
- Support GraphRAG, retrieval-augmented generation, enterprise search, semantic search, and intelligent agents.
- Design semantic frameworks that improve large language model grounding, explainability, traceability, and enterprise knowledge retrieval.
- Partner with AI engineering teams to ensure semantic structures are AI-ready, governed, and usable for enterprise retrieval and decision-support use cases.
- Define enterprise standards, policies, and governance frameworks for ontology management, semantic models, taxonomies, and knowledge graph assets using platforms such as Microsoft Purview and Collibra.
- Provide technical leadership across architecture, engineering, AI, governance, and business teams.
- Conduct architecture reviews and establish best practices for semantic technologies, enterprise data modelling, and AI-ready data ecosystems.
- Mentor architects, engineers, and data stewards on semantic modelling, knowledge graph design, and governed data modelling approaches.
Desired Qualifications
- Experience with data modelling tools such as ER/Studio, Erwin, SAP PowerDesigner, Hackolade, or equivalent.
- Ability to connect business concepts, data models, semantic assets, and AI-ready knowledge structures.
- Ability to work across business subject-matter experts, data architects, data engineers, AI engineers, and governance teams.
- Ability to define semantic standards, ontology governance, knowledge graph design patterns, and enterprise data modelling practices.
- Ability to create explainable, lineage-aware, and governed semantic foundations for analytics, GraphRAG, enterprise search, and AI-enabled decision support.
- Domain experience in wealth and asset management, including familiarity with platforms such as Aladdin, Investrans, or eFront, and concepts such as positions, security master, IBOR, ABOR, and benchmark performance.
About the company
EY (Ernst & Young) provides professional services at a global scale, offering consulting, assurance, tax, and transaction advisory services. It serves clients across industries such as technology, media, real estate, hospitality, and construction. Instead of selling a single product, EY works with clients through tailored engagements where cross-disciplinary teams analyze challenges, design strategies, perform audits, help with tax planning, and assist with mergers or divestitures. What sets EY apart is its worldwide reach and integrated service model, industry-specific expertise, and focus on responsible business practices like sustainability, cybersecurity, and workforce flexibility. EY’s goal is to help organizations improve performance, manage risk, and achieve sustainable growth while building a better working world.
Company Size
5,001-10,000
Company Stage
N/A
Total Funding
N/A
Headquarters
Boston, Massachusetts
Founded
1991
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Simplify's Take
What believers are saying
- EY.ai Agentic for Sales launched March 2, 2026 with Snowflake and Canva.
- EY uses client-zero deployments, scaling Copilot across 400,000 people by May 2026.
- EY’s 2026 AI assurance push monetizes client demand for governance, controls, and risk reviews.
What critics are saying
- Sabah filed RM2.4 billion audit-negligence claims against EY on July 30, 2026.
- EY set aside £188 million in March 2026 after NMC, Shell, and Made.com scrutiny.
- Repeated audit failures can destroy trust, trigger client defections, and shrink the franchise permanently.
What makes Ernst & Young unique
- EY Canvas processes 1.4 trillion journal-entry lines across 160,000 global audits.
- EY-Microsoft invested US$1b+ in May 2026 to industrialize enterprise AI delivery.
- EY launched agentic audit AI on April 7, 2026, spanning Assurance and AI assurance.
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Benefits
- Professional Development Budget
- Flexible Work Hours
- Remote Work Options