What are the responsibilities and job description for the Hiring:: Technical Business Analyst / Capital Markets || NYC, NY / Dallas, TX (Hybrid) position at Key2Source INC?
Role: Business Analyst/Capital Market
Location: NYC, NY / Dallas, TX (Hybrid- 3 days onsite)
Only Local candidates- No relocation
Candidate Visa's : No Opt, No H1B
Job Description: 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. MUST HAVE EXPERIENCE WITH CAPITAL MARKETS SUPPORTING TRADING APPLICATION PROJECTS.
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.