Description

Technical Business Analyst with Capital Markets and Trading Experience
Dallas, TX (Hybrid)
Exp: 10+ Years
Visa: USC, H1B, H4 EAD, L2, E3, TN. GC and GC EAD on W2. 
Need Only Locals. Relocation not allowed. 

We need a senior (10+ years) Technical Business Analyst with experience supporting Finance Data Projects: Financial data structures, KPIs, reporting needs, reconciliation concepts, and data accuracy/compliance expectations. 

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, 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).
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.

Key Skills

Education

Not specified