Principal Data Architect - London Stock Exchange Group in Nottingham

Role Summary

Own the end-to-end data architecture for the Active Data Layer (ADL) programme across LSEG Risk Intelligence Engineering. Define and evolve the data flows, data models, integration patterns, and data assurance architecture that underpin World-Check's full data lifecycle, from acquisition, through curation, to distribution. Drive the data assurance strategy, embedding architectural foundations for data quality, lineage, and governance into ADL from the ground up. Partner with the AI Practice Lead to ensure data architecture enables safe, scalable adoption of AI and agentic capabilities. This is a senior IC role leading through influence and deep technical expertise.

What You’ll Be Doing

• Data Architecture & Data Flows: Own the end-to-end data architecture for ADL: target-state designs, canonical data models, data flow patterns, and platform standards across World-Check's full data lifecycle. Architect data flows between legacy systems, the new curation platform, and customer-facing distribution channels, ensuring traceability, consistency, and completeness. Design the data migration architecture from legacy to the new platform, ensuring data preservation, field mapping correctness, and schema integrity. Define event-driven data flow patterns for real-time propagation, including event schemas, ordering guarantees, and idempotency. Evaluate and recommend data technologies (event streaming, graph databases, data lakes, lakehouses) appropriate to ADL requirements.
• Data Assurance & Quality: Drive the data assurance strategy within ADL, translating preventive, detective, and corrective control frameworks into architectural components. Design data lineage and provenance infrastructure enabling end-to-end traceability from source through curation to publication. Architect data contract and schema governance across pipeline boundaries, structurally enforcing schema conformity and API contract compliance. Define the data quality measurement architecture: where quality dimensions are measured, how thresholds are evaluated, and how results feed dashboards. Design reconciliation architecture supporting regression testing in non-production and continuous reconciliation in production. Architect test data management: synthetic data for development, production-representative data for testing, and golden datasets for regression.
• AI & Agentic Data Enablement: Design data pipelines, feature stores, and access patterns for AI/ML model training, evaluation, and serving. Architect data foundations for agentic curation: data contracts, schema validation, and lineage capture at agent boundaries. Design AI evaluation infrastructure: golden dataset storage, semantic comparison pipelines, and confidence calibration measurement. Define data contract and versioning standards supporting rapid AI experimentation with production-grade reliability.
• Data Modelling, Governance & Platform: Lead conceptual, logical, and physical data model design for ADL domains (screening, due diligence, entity resolution, adverse media). Partner with data governance and compliance teams to embed data ownership, classification, lineage, and access control into the architecture. Architect observability, monitoring, and alerting for production data quality: freshness, completeness, schema drift, and anomaly detection. Provide architectural guidance on cloud-native data services, storage strategies, compute scaling, and cost optimisation.
• Technical Leadership: Act as the senior technical voice for data architecture across ADL, translating complex concepts into clear recommendations for engineering, product, and business stakeholders. Partner with the AI Practice Lead on feasibility, dependencies, risks, and sequencing for AI initiatives. Mentor engineers and data professionals on data modelling, data flow design, and assurance best practices.

What You’ll Bring

• Qualifications & Experience: 10+ years in data architecture or data engineering in complex, large-scale environments; 5+ years in a senior/lead architect capacity. Proven experience designing end-to-end data architectures supporting AI/ML, analytics, and real-time data products. Deep hands-on experience with modern data platforms (e.g., Snowflake, Databricks, Spark, Kafka, Elasticsearch, graph databases, AWS/Azure/GCP). Experience designing data quality and assurance frameworks: data lineage, data contracts, reconciliation, and quality measurement at scale. Background in financial services, risk, compliance, or highly regulated data-intensive environments (preferred). Experience with large-scale data migration programmes and legacy-to-modern platform transitions (preferred).
• Skills & Knowledge: Data Architecture: Data modelling (conceptual, logical, physical), data flow design, event-driven architectures, integration patterns. Data Assurance & Quality: Data lineage, provenance, data contracts, reconciliation, and quality controls embedded across pipelines. AI/ML Data Foundations: Feature stores, ML pipelines, vector databases, embeddings, and data requirements for LLM/agentic systems. Cloud & Platform: Cloud-native data services, infrastructure-as-code, scalable platform patterns. Communication & Influence: Articulating complex decisions to technical and non-technical audiences; influencing without authority.

Nice to Have

• Knowledge graphs, entity resolution, or graph-based models in risk, compliance, or financial crime domains.
• MLOps/LLMOps tooling from a data architecture perspective (feature stores, model registries, eval infrastructure).
• Data assurance or continuous testing frameworks: automated reconciliation, drift detection, quality gate integration into CI/CD.

Career Stage Manager

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