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About the Role

We are seeking client-facing Enterprise Data Architects to join our growing Enterprise AI practice. You will help clients transform fragmented data estates into AI-ready foundations, advising on architecture decisions across cloud data platforms, lakehouse and warehouse patterns, data products, semantic layers, metadata, governance, knowledge graphs, and GenAI retrieval patterns. This is a consulting role where you will diagnose ambiguous client problems, shape options, make trade-offs explicit, and translate complex data architecture issues into clear decisions for both technical teams and executive stakeholders.

Responsibilities

Design AI-ready enterprise data architectures that enable 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, helping them navigate trade-offs between 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 that make enterprise data reusable across analytics, AI, GenAI and operational workflows.
Shape semantic layers, ontologies and knowledge graph patterns where these improve data discoverability, interoperability, explainability or AI consumption.
Oversee high-level design of ingestion, integration and transformation patterns, 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 use of enterprise knowledge.
Act as a trusted advisor to client stakeholders, translating technical architecture concepts into clear business outcomes, options and risks.
Contribute to proposals, client conversations, internal methods and thought leadership on enterprise data architecture and AI-ready data foundations.

Requirements

15+ years experience in data architecture, enterprise architecture, solution architecture or senior data engineering roles.
Strong experience in 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.
Experience with graph modelling, ontology standards or graph query languages such as RDF, OWL and SPARQL.
Familiarity with feature store design and MLOps / DataOps pipeline integration.
Experience with stream processing at scale using Apache Kafka or Apache Flink.
Background in master data management or data mesh architecture.
Consulting or comparable client-facing delivery experience.
Comfortable working in ambiguous consulting environments, shaping options, making trade-offs explicit and taking senior stakeholders on the journey from strategy to implementation.
Self-directed, able to prioritise and juggle multiple workstreams.
Clear communicator who can simplify complexity for technical and non.-technical audiences alike.
Collaborative, curious, continuous learner.

Skills

Data Governance
Data Strategy
Cloud Data Platforms
Lakehouses
Warehouses
Data Integration
Data Modelling
Metadata Management
Semantic Layers
Ontologies
Knowledge Graphs
GenAI
Retrieval-Augmented Generation
Vector Search
Stakeholder Management
Communication
Graph Modelling
Ontology Standards
Graph Query Languages (RDF, OWL, SPARQL)
Feature Store Design
MLOps
DataOps
Stream Processing (Apache Kafka, Apache Flink)
Master Data Management
Data Mesh Architecture
Consulting
Client-Facing Delivery
Databricks
Snowflake
Microsoft Fabric
Azure Synapse
Google BigQuery
Amazon Redshift
Spark
dbt
Airflow
Azure Data Factory
AWS Glue
Dataflow
Microsoft Purview
Collibra
Informatica
Alation
Atlan
OpenLineage
Neo4j
Amazon Neptune
Stardog
GraphDB
Pinecone
Weaviate
Milvus
Azure AI Search
OpenSearch
pgvector
Feast
Tecton
MLflow

Location

UK
International

Work Type

Consulting
Client-Facing
Full-time

Experience Level

15+ years experience
Senior

Education Level

Bachelor’s degree or equivalent experience

Benefits

Industry-leading compensation
Benefits
Top training and development opportunities

About the Company

Our Enterprise AI practice supports large global organisations to find and deliver business value from data and AI.
We work within the broader Infosys ecosystem, partnering with deep industry and domain experts to identify transformational opportunities and help clients bridge from strategy through to implementation.
We collaborate closely with our global delivery organisation to turn ideas into scalable solutions.
Examples of our work include developing AI transformation roadmaps, setting up AI Centres of Excellence, data strategy and readiness assessments, automating complex workflows with multi-agent systems and intelligent document processing with generative AI.
We always bring a human-centred and value-led approach to technology transformation.
Be part of a globally renowned management consulting firm on the front-line of industry disruption and at the cutting edge of technology.
We work with market leading brands across sectors.
Our culture is inclusive and entrepreneurial.
Being a mid-size consultancy within the scale of Infosys gives us the global reach to partner with our clients throughout their transformation journey.
Our core values, IC-LIFE, form a common code that helps us move forward. IC-LIFE stands for Inclusion, Equity and Diversity, Client, Leadership, Integrity, Fairness, and Excellence.
Within Europe, we are recognized as one of the UK’s top firms by the Financial Times and Forbes due to our client innovations, our cultural diversity and dedicated training and career paths.
Infosys is on the Germany’s top employers list for 2023.
Management Consulting Magazine named us on their list of Best Firms to Work for.
Furthermore, Infosys has been recognized by the Top Employers Institute, a global certification company, for its exceptional standards in employee conditions across Europe for five years in a row.

Equal Opportunity

Inclusion, Equity and Diversity