Business analyst​/Capital Markets - Dallas,TX; Hybrid

Job Title: Business analyst / Capital Markets

Location: Dallas,TX (Hybrid - 3 days in a week)

Experience: 10+ years

Relocation: NO RELOCATION

• 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.

• 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, hierarchies
  • Star schema alignment, calculated measures, reusable datasets
  • Consistency across Power BI/Tableau and downstream AI/ML consumers
• Azure (Preferred):
  • Azure SQL Database/SQL Server
  • Azure Data Factory (ADF)
  • Azure Databricks
• ETL/ELT & BI Tools:
  Familiarity with orchestration tools and exposure to Power BI and/or Tableau (semantic models/datasets).

• 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.

• 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.