Senior Data Analyst

About DeepLightAI DeepLightAI is a specialist AI and data consultancy with extensive experience implementing intelligent enterprise systems across multiple industries, with depth in financial services and banking. Our team combines deep expertise in data science, statistical modeling, AI/ML technologies, workflow automation, and systems integration with a practical understanding of complex business operations. The Senior Data Analyst delivers enterprise-wide data foundations in a complex banking environment. The role combines enterprise/domain data modelling, hands‑on data analysis, and practical data architecture input to improve how data is structured, understood, and trusted across the organisation. We value candidates who bring a modern, forward‑looking approach, e.g., exposure to AI‑assisted modelling/discovery and an understanding of how data platforms, metadata and semantic concepts can improve modelling productivity and consistency. Responsibilities Designing and evolving conceptual and logical data models across core banking domains. Performing hands‑on SQL analysis to profile datasets, validate definitions, reconcile discrepancies, and identify gaps. Providing modelling‑aligned architecture inputs (conformed entities, keys, reference/master data approach, integration patterns, semantic layer considerations). Driving standardisation of data definitions and produce practical artefacts (data dictionary/glossary, mappings, documentation). Partnering with delivery teams to support metadata, lineage, and governance‑aligned documentation, and ensure modelling decisions are adopted. Benefits & Growth Opportunities Competitive salary and performance bonuses Comprehensive health insurance Professional development and certification support Opportunity to work on cutting‑edge AI projects Flexible working arrangements Career advancement opportunities in a rapidly growing AI company This position offers a unique opportunity to shape the future of AI implementation while working with a talented team of professionals at the forefront of technological innovation. The successful candidate will play a crucial role in driving our company's success in delivering transformative AI solutions to our clients. At DeepLightAI, we recognise that diversity drives innovation. We are committed to fostering an inclusive environment where individuals with different thinking styles can thrive and contribute their unique strengths to our specialised AI and data solutions. Our goal is to ensure our application and interview process is accessible, predictable, and fair for all candidates. If you require any specific adjustments to the application process, or if you require any reasonable adjustments should you be successful in being processed to the interview stage, please do let us know. This information will be kept strictly confidential and will not impact hiring decisions. Qualifications What we need you to have: 5–10+ years in hands‑on data analysis and/or data modelling, ideally in banking/financial services. Strong conceptual/logical modelling capability and ability to translate business needs into implementable structures. Strong SQL and confidence working directly with complex datasets. Strong stakeholder engagement and clear communication/documentation skills. Enterprise data modelling (banking‑grade): can structure domains end‑to‑end (customer/account/product/transaction etc.) with clear keys, relationships, and definitions. Hands‑on data analyst mindset: strong SQL + profiling/reconciliation; comfortable working directly with messy real‑world bank data to prove what’s true. Data architecture awareness: can design models that land well in a lakehouse (layering, conformed entities, semantic alignment) and work across relational + semi/unstructured data. Delivery + stakeholder execution: can run working sessions, drive decisions, produce usablefacts (dictionary/glossary/mappings) and keep momentum with platform/engineering teams. What we'd like you to have: Forward‑thinking modelling: exposure to AI/GenAI‑assisted modelling/discovery (automation of documentation/metadata, semantic modelling concepts, knowledge‑graph thinking). Data quality & observability orientation: experience defining critical elements, data quality checks/metrics, and supporting “data contracts”/early issue detection patterns. Lakehouse + enterprise BI/semantic familiarity: understands how models support analytics/MIS‑style consumption and tool rationalisation decisions. Consulting background (nice signal): Big 4 / Accenture / Capgemini or similar (optional, not required). #J-18808-Ljbffr