At a Glance
- Tasks: Lead the design of end-to-end data architecture for innovative AI and analytics projects.
- Company: Join a leading financial services firm focused on risk intelligence and data innovation.
- Benefits: Attractive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative environment with mentorship opportunities and a focus on career advancement.
- Why this job: Shape the future of data architecture and influence cutting-edge AI initiatives.
- Qualifications: 10+ years in data architecture with strong experience in modern data platforms.
The predicted salary is between 80000 - 100000 € per year.
Requirements
- 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)
- 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
- (Desirable) Knowledge graphs, entity resolution, or graph-based models in risk, compliance, or financial crime domains
- (Desirable) MLOps/LLMOps tooling from a data architecture perspective (feature stores, model registries, eval infrastructure)
- (Desirable) Data assurance or continuous testing frameworks: automated reconciliation, drift detection, quality gate integration into CI/CD
What the job involves
- 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
- 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
- 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
- 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
- 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
- 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
Principal Data Architect in Nottingham employer: Deepstreamtech
As a Principal Data Architect at LSEG, you will thrive in a dynamic and innovative environment that champions collaboration and continuous learning. Our commitment to employee growth is reflected in our robust training programmes and mentorship opportunities, ensuring you stay at the forefront of data architecture advancements. Located in a vibrant city, we offer a flexible work culture that values work-life balance, making LSEG an exceptional employer for those seeking meaningful and rewarding careers in the financial services sector.
StudySmarter Expert Advice🤫
We think this is how you could land Principal Data Architect in Nottingham
✨Tip Number 1
Network like a pro! Attend industry meetups, webinars, and conferences related to data architecture. It's a great way to meet potential employers and get your name out there.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your past projects, especially those involving data architecture and AI/ML. This will give you an edge when chatting with recruiters.
✨Tip Number 3
Don’t just apply; engage! When you find a job on our website, reach out to someone in the company on LinkedIn. A friendly message can make a huge difference in getting noticed.
✨Tip Number 4
Prepare for interviews by brushing up on your communication skills. You’ll need to explain complex data concepts clearly to both technical and non-technical folks. Practice makes perfect!
We think you need these skills to ace Principal Data Architect in Nottingham
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter to highlight your experience in data architecture and engineering. We want to see how your skills align with the requirements, especially in AI/ML and real-time data products.
Showcase Your Technical Skills:Don’t hold back on detailing your hands-on experience with modern data platforms like Snowflake or Databricks. We love seeing specific examples of how you've designed data flows and architectures in your previous roles.
Communicate Clearly:Remember, you’ll need to articulate complex ideas to both technical and non-technical audiences. Use clear language in your application to demonstrate your communication skills and ability to influence without authority.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity in our team!
How to prepare for a job interview at Deepstreamtech
✨Know Your Data Architecture Inside Out
Make sure you can discuss your experience with data architecture in detail. Be prepared to explain how you've designed end-to-end data architectures, especially those supporting AI/ML and real-time data products. Use specific examples from your past roles to illustrate your expertise.
✨Showcase Your Hands-On Experience
Highlight your hands-on experience with modern data platforms like Snowflake, Databricks, and Kafka. Be ready to discuss how you've implemented these technologies in large-scale environments and the impact they had on your projects. This will demonstrate your practical knowledge and problem-solving skills.
✨Communicate Complex Ideas Simply
Since this role involves articulating complex decisions to both technical and non-technical audiences, practice explaining your past projects in simple terms. Think about how you can break down intricate concepts into digestible pieces that anyone can understand, showcasing your communication skills.
✨Prepare for Scenario-Based Questions
Expect scenario-based questions that assess your ability to handle challenges in data migration, quality assurance, and architecture design. Prepare by thinking through potential scenarios you might face in the role and how you would approach them, demonstrating your strategic thinking and adaptability.
