Data Architecture 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 tools to develop logical, physical, and conceptual data models for portfolio and fund accounting data.
ETL Processes: 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 Stakeholders: Work closely with business stakeholders, data scientists, analysts, and IT teams to ensure that data solutions meet the company's reporting and analytical requirements.
Optimization & 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.