Enterprise Data Architect

Infosys Consulting is a globally renowned management consulting firm that supports large global organisations to find and deliver business value from data and AI. Their Enterprise AI practice helps clients transform fragmented data estates into AI-ready foundations and works closely with global delivery teams to implement transformational solutions. The firm is recognized among the UK’s top consulting firms and is part of the large Infosys organization, known for innovation and rapid growth.

• 5–10+ years in data architecture, enterprise architecture, solution architecture or senior data engineering roles.
• 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 in data governance initiatives, including catalogues, lineage, ownership, stewardship, data quality and metadata management.
• Practical understanding of semantic layers, ontologies or knowledge graph concepts.
• 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; 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%.

Preferred Skills:

• 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 using Apache Kafka or Apache Flink.
• Background in master data management or data mesh architecture.
• Consulting or comparable client-facing delivery experience.
• Exposure to technologies such as: 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, Pinecone, Weaviate, Milvus, Azure AI Search, OpenSearch, pgvector, Feast, Tecton, MLflow.

• 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.