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Infosys

Global digital services and consulting provider

Enterprise Data Architect Consultant

Data ManagementConfirmed live in the last 24 hours
Full-timeLondon, UKNo salary listed

About the Job

Requirements

  • At least 15 years of 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, artificial intelligence, machine learning, or generative AI 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 Amazon Web Services, Microsoft Azure, or Google Cloud, and familiarity with leading lakehouse or warehouse technologies.
  • Understanding of how data architecture decisions affect artificial intelligence and generative AI outcomes, including data quality, provenance, context, retrieval, security, privacy, and semantic consistency.
  • Familiarity with generative AI data patterns such as retrieval-augmented generation, vector search, embedding pipelines, chunking strategies, or enterprise search.
  • Strong stakeholder management and communication skills, including the ability to present complex technical trade-offs clearly to non-technical sponsors and senior executives.
  • Excellent written and verbal communication skills in English.
  • A bachelor’s degree or equivalent experience; quantitative, technical, or analytical disciplines are advantageous.
  • Willingness to travel up to around 60%, depending on project requirements, across the UK and internationally.

Responsibilities

  • Design AI-ready enterprise data architectures that enable analytics, AI, machine learning, generative AI, 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, generative AI, 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.

Desired Qualifications

  • Experience with graph modelling, ontology standards, or graph query languages such as RDF, OWL, and SPARQL.
  • Familiarity with feature store design and MLOps or 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.
  • Exposure to relevant technologies, or comparable alternatives, including Databricks, Snowflake, Microsoft Fabric, Azure Synapse, Google BigQuery, Amazon Redshift, Spark, dbt, Airflow, Azure Data Factory, AWS Glue, Dataflow, Kafka, Flink, Microsoft Purview, Collibra, Informatica, Alation, Atlan, OpenLineage, Neo4j, Amazon Neptune, Stardog, GraphDB, RDF, OWL, SPARQL, vector databases and search platforms such as Pinecone, Weaviate, Milvus, Azure AI Search, OpenSearch, or pgvector, feature stores such as Feast or Tecton, and model lifecycle and experiment tracking tools such as MLflow.
  • Comfort working in ambiguous consulting environments, shaping options, making trade-offs explicit, and guiding senior stakeholders from strategy to implementation.
  • Ability to work self-directed, prioritise, and manage multiple workstreams.
  • Ability to communicate clearly and simplify complexity for technical and non-technical audiences.
  • Collaborative, curious, continuous-learning approach.

About the Company

Infosys

Global digital services and consulting provider

Infosys is a global provider of digital services and consulting. It helps organizations with software development, cloud solutions, data and analytics, automation, and AI-powered platforms to transform their technology and business operations. The company primarily delivers these capabilities through client projects, outsourcing, and managed services, rather than selling a single standalone product. Infosys differentiates itself by its long-standing global reach (present in over 50 countries) and its breadth of offerings, including a dedicated consulting arm and a business process outsourcing unit, which together cover strategy, design, development, and ongoing operations. Its goal is to guide clients through digital transformation and AI adoption at scale, helping them improve efficiency, agility, and competitiveness over the long term.

Company size
10,001+
Company stage
IPO
Headquarters
Bengaluru, India
Founded
1981

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