Data Architecture and Design: Design and implement scalable and efficient data architectures to support the company's financial data needs, leveraging Snowflake for cloud-based data solutions.
Data Modelling: Use Erwin or similar tool to develop logical, physical and conceptual data models for portfolio and fund accounting data.
ETL Process: Collaborate with Data Engineers to build and optimize ETL pipelines to process large datasets from various financial systems.
Integration with Financial systems: Ensure seamless integration of data between accounting systems, portfolio management systems, and data warehouse solutions.
Data Governance: Establish Data governance practices, including data quality, data security, and metadata management.
Collaboration with Stakeholder Management: Work closely with business stakeholders, data scientist, analysts, and IT teams to ensure that data solutions meet the company's reporting and analytical requirements.
Optimizing & Performance Tuning: Continuously monitor and optimize data architecture and performance to handle high volumes of financial transactions and reporting.
Documentation: Create comprehensive documentation on data architecture, models, and processes to ensure consistency and alignment across teams.