Technical Business Analyst

Technical Business AnalystLocation: Dallas, TX - Hybrid 3 Days a Week - NO RELOCATIONType of Interview Required: VideoVisa: No OPT, CPT and H1BEnd Client: Mayfair CapitalNote: Candidate must have recent experience supporting Azure Data/Data warehouse projects in a financial services, banking or Capital Market Enterprises. Need someone with financial domain experience.Client would like to see certifications as well and must be within one hour commute to the Dallas/FT Worth metroplex.Job DescriptionTechnical Business Analyst (10+ years) - Looking for more of a data modeler/architect with expertise in financial services. Primary skills (must-have):Finance Domain Knowledge: Financial data structures, KPIs, reporting needs, reconciliation concepts, and data accuracy/compliance expectations.Data Modeling & Analysis: Strong capability in dimensional/logical modeling, data profiling, data quality analysis, and translating business logic into data structures.Expert SQL: Advanced SQL for extraction, transformation/validation, performance tuning, and supporting analytics/reporting use cases.Core technical requirements:Data Warehouse Expertise: Data architecture, ingestion/integration patterns, governance, lineage, and warehouse best practices.Semantic Layer Design (Critical): Experience defining and managing a semantic layer for enterprise reporting and AI, including:Business definitions/metric logic, conformed dimensions, hierarchiesStar schema alignment, calculated measures, reusable datasetsConsistency across Power BI/Tableau and downstream AI/ML consumersAzure (Preferred):Azure SQL Database/SQL ServerAzure Data Factory (ADF)Azure DatabricksETL/ELT & BI Tools: Familiarity with orchestration tools and exposure to Power BI and/or Tableau (semantic models/datasets).Key responsibilities:Requirements & Metric Definition: Gather/reporting & AI requirements; define KPIs, business rules, and data contracts; translate into technical specs for warehouse + semantic layer.Data Analysis & Validation: Profile data, identify gaps, perform reconciliation and data quality checks; ensure finance metrics are correct and auditable.Data Modeling: Design/maintain logical and dimensional models to support reporting and AI feature readiness.Semantic Layer Delivery: Partner with BI/engineering to implement governed semantic models (definitions, measures, hierarchies, security assumptions as needed).Collaboration with Data Engineers: Ensure pipelines/ETL align with modeling and semantic requirements; support schema optimization and efficient query patterns.Documentation: Maintain requirements, mappings, metric definitions, data dictionaries, and semantic layer specifications.Continuous Improvement: Recommend best practices/tools to improve scalability, reuse, and consistency across reporting and AI.Soft skills:Strong stakeholder management; able to translate business needs into technical deliverables.High attention to detail, strong prioritization, and ability to work independently in a fast-paced environment.