Technical Business Analyst

Technical Business Analyst

Location: Dallas, TX - Hybrid 3 Days a Week - NO RELOCATION

Type of Interview Required: Video

Visa: No OPT, CPT and H1B

End Client: Mayfair Capital

Note: 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 Description

Technical 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, 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.