Enterprise Data Architect

Infosys Consulting

2d•Hybrid

About The Position

Enterprise AI is forcing organisations to rethink their data estates. Data platforms designed mainly for reporting are often not enough for GenAI, semantic search, agentic workflows and AI-enabled decision-making. Clients now need data that is trusted, governed, contextualised and consumable by both people and intelligent systems. We are looking for 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, lineage, governance, knowledge graphs and GenAI retrieval patterns. This is a consulting role, not a purely internal architecture role. 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. You will work in cross-functional teams alongside product owners, data scientists, ML and GenAI engineers, data engineers, business analysts and client stakeholders. Typical outputs may include 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. We are hiring across several levels. At earlier levels, we expect strong architecture delivery experience and hands-on platform understanding. At senior levels, we expect the ability to shape enterprise data strategy, influence senior stakeholders, lead complex architecture decisions and guide multi-disciplinary delivery teams. We do not expect every candidate to be a specialist in every aspect of AI-ready data architecture. We are looking for architects with strong core data architecture experience and credible depth in some of the areas that matter for AI-enabled data estates, such as governance, semantic modelling, lakehouse architecture, data products, metadata management, knowledge graphs, RAG or enterprise data strategy.

Requirements

5–10+ years, depending on level, in data architecture, enterprise architecture, solution architecture or senior data engineering roles.
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.

Nice To Haves

A second major European language is an advantage.
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.
Exposure to some of the following, or comparable, technologies is useful. We do not expect candidates to have worked with all of them: o Cloud data platforms, warehouses and lakehouses: Databricks, Snowflake, Microsoft Fabric, Azure Synapse, Google BigQuery, Amazon Redshift o Data engineering and orchestration: Spark, dbt, Airflow, Azure Data Factory, AWS Glue, Dataflow, Kafka, Flink o Governance, catalogue and lineage: Microsoft Purview, Collibra, Informatica, Alation, Atlan, OpenLineage o Graph, ontology and semantic technologies: Neo4j, Amazon Neptune, Stardog, GraphDB, RDF, OWL, SPARQL o AI/ML data infrastructure: vector databases and search platforms such as Pinecone, Weaviate, Milvus, Azure AI Search, OpenSearch or pgvector; feature stores such as Feast or Tecton; model lifecycle and experiment tracking tools such as MLflow
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

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.