Lead Business System Analyst

A Lead Business Systems Analyst in Data Management ensures that business data needs are clearly defined, governed, and correctly implemented across systems, turning raw data into trusted, actionable information.

The Lead BSA ensures that:

• Business data requirements are clearly defined and understood, supported with in-depth data analysis, and organizational feasibilities
• Data flows correctly across the best systems of truth
• Data quality, lineage, and governance standards are met
• Data solutions align with business objectives and regulatory needs

• Gather and document business data needs
• Translate business questions into data elements, metrics, and definitions
• Define functional and non-functional requirements for data systems
• Map data from source systems to target systems (e.g., operational systems → data lake/warehouse)
• Document data transformations, rules, and dependencies
• Support data lineage and traceability for audits and compliance

• Work with Data Governance teams on:
• Business glossaries
• Data ownership and stewardship
• Reference and master data definitions
• Help resolve data issues and root causes

• Analyze how data flows across upstream and downstream systems
• Identify gaps, redundancies, or risks in data processes
• Support system enhancements, migrations, and integrations

• Partner with:
• Architects
• Reporting and analytics teams
• Clarify requirements, review designs, and validate solutions
• Participate in sprint planning, backlog grooming, and UAT

• Define data validation and reconciliation criteria
• Support UAT by validating:
• Aggregations and calculations
• Report outputs against business expectations

• Act as the single point of contact between business and IT for data-related initiatives
• Translate technical concepts into business-friendly language
• Ensure alignment across teams

• Business Data Requirements Documents (B-DRDs)
• Functional Requirements / User Stories
• Source-to-target mappings (STTM)
• Data definitions and metric logic
• Data quality rules and acceptance criteria
• UAT test scenarios and validation results

• Strong understanding of business processes
• Ability to ask the right data questions
• Stakeholder management and communication

• Data warehousing concepts
• ETL/ELT processes
• SQL/Python (for data validation and analysis)
• Metadata, lineage, and data modeling concepts
• Familiarity with data governance and regulatory requirements