Lead Business Analyst

Role Summary

The Lead / Senior Business Analyst is responsible for driving requirements discovery, cross‑functional alignment, and solution clarity across a large‑scale, multi‑stream data transformation programme. This role blends advanced business analysis with selected project‑management responsibilities, enabling end‑to‑end delivery of complex data workstreams while maintaining strong relationships with internal and external stakeholders.

The role will operate across discovery, requirements definition, data onboarding, solution design execution , testing coordination and support, and readiness activities, ensuring clarity, traceability, and quality of all business requirements.

Key Responsibilities

Requirements Leadership & Analysis

• Lead discovery sessions to understand business processes, data flows, operating models, and functional/non‑functional requirements.
• Document clear, structured requirements, user stories, and acceptance criteria across data ingestion, data mastering, data quality, and downstream integration.
• Reformulate unclear requirements and ensure full alignment across business, technical, and operations stakeholders.

Stakeholder Engagement & Relationship Management

• Act as the primary liaison between internal delivery teams, data SMEs, engineering, operations, and client teams.
• Build strong relationships with senior internal and external stakeholders; articulate progress effectively in structured forums.
• Manage expectation setting, requirement validation, and change impacts with diplomacy and clarity.

Cross‑Stream Coordination (Hybrid PM Responsibilities)

• Own workstream planning, scheduling, RAID management, and cross‑team alignment.
• Coordinate activities across requirements, data onboarding, engineering, solution design, testing, and go‑live readiness.
• Provide RAG status updates and ensure inter‑stream dependencies and risks are well understood.

Testing, Validation & Cutover Support

• Support testing strategy design and ensure requirements traceability through UAT, data validation, and cutover events.
• Coordinate automated tests, foundational data checks, and AB comparisons across old vs. new data platforms.

Data Coordination, Analysis & Gap Assessment

• Coordinate data‑related activities with internal teams (e.g., Data Management, Operations, Engineering) and with client stakeholders to ensure all data requirements are clearly understood, documented, and sequenced appropriately
• Conduct data analysis, including reviewing existing datasets, mappings, data structures, business rules, and transformations to identify gaps, ambiguities, or inconsistencies that impact downstream processes.
• Perform gap analysis between current‑state and target‑state data models, data onboarding requirements, and operational workflows.
• Ensure all data requirements, validation rules, and dependencies are captured clearly and communicated to the relevant technical and operational teams.
• Track progress of data readiness activities (dataset preparation, mapping completion, validation steps) and ensure they align with workstream milestones, without owning any vendor or licensing tasks.

Governance & Reporting

• Produce clear workstream packs, status updates, requirement traceability matrices, and risk/issue reports.
• Present updates to Working Groups and Steering Committees when required.

Key Requirements:

• 5+ years in business analysis and/or project management, ideally within financial services, investment data, or enterprise data platforms. 
• Strong background leading multi-stream, complex, or data-heavy transformation programs  within financial services, investment data, or enterprise data platforms.  
• Hands-on experience with:  Requirements gathering, user stories, acceptance criteria. , Cross-functional delivery across data, engineering, operations, and testing.RAID, RAG reporting, dependency management, and structured governance. 
• Deep practical knowledge of investment data domains (Security Master, Price Master, Issuer Master, benchmarks/index data) 
• Bonus: exposure to cloud data platforms (Databricks, Snowflake) and regulated or high‑control environments. 

Preferred

• Experience with cloud‑based data platforms (Databricks, Snowflake, or similar).
• Exposure to regulated environments or high‑control data operations.
• Familiarity with investment data, market data onboarding, or securities reference data.