Enterprise Data Architect – Consultant

Infosys

. Design AI-ready enterprise data architectures enabling analytics, AI, ML, GenAI and agentic applications to consume data accurately, securely and with appropriate business context .

Posted 11/7/2026full-timeLondon • United KingdomSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in designing AI-ready enterprise data architectures and implementing robust data governance frameworks. Proficient in translating complex technical concepts into actionable business strategies while collaborating effectively with cross-functional teams.

Highest-signal resume keywords

Data Architecture DesignData Governance Policy FormulationCloud Data Platforms (AWS, Azure, Google Cloud)Semantic Layers and Knowledge GraphsStakeholder Management and Communication

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Applicant Tracking System Keywords

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Hard Skills

Data IntegrationData ModellingMetadata ManagementData Quality AssuranceGraph DatabasesEvent-Driven ArchitecturesReal-Time Data ProcessingBatch ProcessingData ContractsData Lifecycle Management

Soft Skills

Clear CommunicationCollaborationSelf-DirectionCuriosityContinuous Learning

Tools & Technologies

Apache KafkaApache FlinkGraph Query Languages (RDF, OWL, SPARQL)Feature Store DesignMLOps/DataOps

Industry Keywords

Enterprise ArchitectureSolution ArchitectureRegulated IndustriesAI and ML ConsumptionData Strategy Roadmaps

Tech Stack

Tools & technologies

ApacheAWSAzureCloudKafka

About the role

Key responsibilities & impact

Design AI-ready enterprise data architectures enabling analytics, AI, ML, GenAI and agentic applications to consume data accurately, securely and with appropriate business context
Assess clients’ existing data estates, diagnose structural, governance, semantic and quality issues, and design pragmatic modernisation roadmaps
Advise clients on architecture and platform choices across lakehouses, warehouses, data fabrics, graph databases, semantic layers, vector search and hybrid architectures
Define data governance and metadata patterns covering ownership, stewardship, quality, lineage, cataloguing, access control and data lifecycle management
Design data products, data contracts and information models for reusable enterprise data
Shape semantic layers, ontologies and knowledge graph patterns
Oversee high-level ingestion, integration and transformation designs, including batch, event-driven and real-time architectures
Identify and mitigate data-related risks, including poor data quality, weak provenance, data leakage, inappropriate access, retrieval failure and inference-time enterprise knowledge use
Act as a trusted advisor, translating technical architecture concepts into business outcomes, options and risks
Contribute to proposals, client conversations, internal methods and thought leadership
Work in cross-functional teams with product owners, data scientists, ML and GenAI engineers, data engineers, business analysts and client stakeholders
Produce target-state architectures, maturity assessments, platform option appraisals, data product designs, governance models, lineage maps, ontology and semantic models, integration patterns, GenAI data-readiness assessments and implementation roadmaps

Requirements

What you’ll need

15+ years experience in data architecture, enterprise architecture, solution architecture or senior data engineering roles
Strong experience working with clients to formulate Data Governance policy and Data Strategy roadmaps
Experience modernizing data landscapes and establishing robust governance frameworks in regulated industries
Demonstrable experience designing modern data architectures for analytics, AI, ML or GenAI consumption
Strong understanding of enterprise data architecture patterns, including cloud data platforms, lakehouses, warehouses, data integration, data modelling and metadata management
Experience contributing to or leading data governance initiatives, including catalogues, lineage, ownership, stewardship, data quality and metadata management
Practical understanding of semantic layers, ontologies or knowledge graph concepts, with hands-on experience in at least one of these areas
Deep experience with at least one major cloud data platform, such as AWS, Azure or Google Cloud, and familiarity with leading lakehouse or warehouse technologies
Understanding of how data architecture decisions affect AI and GenAI outcomes, including data quality, provenance, context, retrieval, security, privacy and semantic consistency
Familiarity with GenAI data patterns such as retrieval-augmented generation, vector search, embedding pipelines, chunking strategies or enterprise search
Strong stakeholder management and communication skills, with the ability to present complex technical trade-offs clearly to non-technical sponsors and senior executives
Excellent written and verbal communication skills in English
Bachelor’s degree or equivalent experience; quantitative, technical or analytical disciplines are an advantage
Willingness to travel, up to around 60% depending on project requirements, across the UK and internationally
Preferred: experience with graph modelling, ontology standards or graph query languages such as RDF, OWL and SPARQL
Preferred: familiarity with feature store design and MLOps/DataOps pipeline integration
Preferred: experience with stream processing at scale using Apache Kafka or Apache Flink
Preferred: background in master data management or data mesh architecture
Preferred: consulting or comparable client-facing delivery experience
Comfortable working in ambiguous consulting environments
Self-directed, able to prioritise and juggle multiple workstreams
Clear communicator who can simplify complexity for technical and non-technical audiences
Collaborative, curious, continuous learner

Benefits

Comp & perks

Industry-leading compensation and benefits
Top training and development opportunities
Training and career paths
Inclusive and entrepreneurial culture
Global reach and opportunity to work with market-leading brands across sectors