Financial Data Modeling_Vice President_Pune

Engage with finance, tech, client org, and data engineering teams to gather requirements, understand application processing, identify gaps and align build models for business needs.

Average day is highly collaborative, focusing on reaching out to application and users to understand client metrics and logic within their systems. Throughout the day modeler spends significant time mapping data lineage, tracing how data flows from source systems through various transformations and engage in hands-on data modeling, codifying definitions and implementing standard recommendations based on their learning.

Key Responsibilities:

• Develop good understanding of various metrics used in calculations and reporting of Return on Tangible Common Equity (RoTCE)
• Apply a methodological approach to decompose calculation methods used for various metrics in RoTCE
• Ensure a clear central definition of each metric and how it is used across various lenses, including business unit, products, geography time periods.
• Lead assessment of end-to-end data flows for all data elements used in a particular metrics, including coordinating with the business in identifying critical data, defining standards and quality expectations, and prioritize remediation of data issues.
• Establish an inventory of data elements required to perform Client RoTCE calculations, allocations and reporting
• Produce a gap assessment of the current vs. strategic sourcing
• Identify appropriate strategic source for critical data elements used in calculation & reporting of RoTCE
• Build conceptual and logical data models for all metrics used in RoTCE
• Create a matrix of tactical vs strategic CSIs and frequency of data availability
• Implementation of stringent data quality rules to ensure accuracy, completeness and consistency of the financial data.
• Draft detailed specification containing calculations, data transformations and aggregation logic for each metric used in Client RoTCE to tech teams

Skills & Qualification

• 10+ years of combined experience in banking and financial services industry, information technology and/or data controls and governance.
• Preferably Engineering Graduate with Post Graduation in Finance
• Extensive experience in the capital markets business and processes
• Deep understanding of different products (i.e., derivatives, FX, securities trading and securities financing)
• Sound knowledge about Accounting, Risk Management Concepts
• Experience with data management processes and tools and applications, including process mapping and lineage toolsets.
• Strong knowledge of structured/unstructured databases, data modeling, data management, rapid / iterative development methodologies and data governance tools.
• Strong understanding of data governance issues, policies, regulatory requirements, and industry information affecting the business environment.
• Demonstrated stakeholder management skills.
• Actively managed various aspects of data initiatives including analysis, planning, execution, and day-to-day production management.
• Technical Skills / Knowledge – Understand schema / models, can explain difference between conceptual, logical, physical models, relational models, understands inheritance concepts for models and has understanding of SDLC process
• Technology / Program experience – Open API specifications (Swagger), JSON schema, Tools: VSstudio, GitHub, lightspeed
• Soft Skills – Analytical thinking – ability to break down complex data structures and processes to identify issues and develop logical models that meet business needs.
• Communication skills – needs to be able to communicate with technical and non-technical stakeholders to gather requirement, express needs and develop clear documentation
• Languages Required – English
• Excellent presentation skills, business and technical writing, and verbal communication skills to support decision-making and actions
• Excellent problem-solving and critical thinking skills to recognize and comprehend complex data flow and designs.
• Self-motivated and able to dynamically determine priorities
• Experience with big data technologies: Hadoop, spark or snowflake
• Data visualization skills – can help in creating visual representation of data models and provide input to UX / UI team to help make it easier to communicate complex model relationships with stakeholders

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