Job Title: Technical Business Analyst/ Data analyst/ Data engineering

Location: Dallas, TX (Onsite)

Exp: 10+ Years 

Job Description

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.