Specd

Lead the design and maintenance of enterprise-grade dimensional models using Kimball standards. Design fact tables, dimensions, conformed dimensions, and surrogate key strategies. Ensure models are optimized for Redshift performance (distribution styles, sort keys, query patterns). Review and refactor existing models to improve usability, performance, and scalability. Write and optimize complex SQL queries on Amazon Redshift for analysis, validation, and performance tuning. Profile source data to validate assumptions and inform modelling decisions. Support downstream analytics and BI teams with query optimization and model usage guidance. Partner with data engineers and analysts to align modelling decisions with ELT implementation. Facilitate requirements and modelling workshops with business stakeholders and data knowledge workers. Translate business concepts, KPIs, and metrics into well-defined dimensional structures. Act as a trusted advisor to business and analytics teams on data interpretation and usage. Communicate complex data concepts clearly to non-technical audiences. Define and enforce modelling standards, naming conventions, and design patterns. Document data definitions, business rules, and assumptions. Support data lineage, impact analysis, and enterprise data consistency. Contribute data governance and best-practice frameworks. Provide guidance and mentoring to junior modelers and analysts. Review data models and SQL developed by others. Influence architectural and design decisions across the data platform. Relevant qualification in a related field, e.g., Computer Science, Data Science, Business Analytics, Information Management. 8 – 10 years’ experience within a Data role. Telecommunications experience 5+ years’ experience. Banking experience 5+ years’ experience. Experience with AWS data ecosystem (S3, Glue, Athena, etc.). Familiarity with BI tools and semantic layers. Exposure to data governance, metadata, or data catalog tools. Experience in Agile / product-oriented data teams. Extensive experience in enterprise data warehousing. Advanced expertise in Kimball dimensional modelling. Proven experience designing models used at scale across multiple business domains. Strong understanding of analytical and reporting use cases. Advanced SQL skills , with a strong focus on Amazon Redshift and other data/analytic platforms. Experience optimizing queries for performance in columnar data warehouses. Strong data profiling and validation skills. Solid understanding of ELT/ETL processes and data pipelines. Proven ability to run effective modelling and requirements workshops. Experience working closely with data knowledge workers , analysts, and decision-makers. Ability to challenge requirements constructively and propose better modelling approaches. Compensation will be determined based on qualifications, applicable banking experience, and previous earnings.