Banking Financial Data
Extract data from the institution's data warehouse. Typically this involves a complex process of extracting data from a multitude of tables within the data warehouse. Bringing together the necessary information for a range of different tasks and projects.
For example: extracting all data around a customer's mortgage, its interest rate or their loan repayment history.
Migrate the existing database to a new more efficient and cost-effective process. This could also include the migration of a new reporting tool to streamline the reporting process.
For example: moving the location of a database to a cloud-based platform. Migrating the reporting process from OpenSAS to Teradata.
Automate the reporting process to remove the manual cost and reporting errors associated with a manual process.
For example: Extracting data directly from the database and insert that directly into an excel spreadsheet while cross-checking all figures to ensure accuracy across all reports.
Automate the trade execution process for a given set of trading criteria. Integrating the process into a brokerage account. Fully or semi-automated, giving the portfolio manager final say on the execution. This automation process is focused on long term trading decisions.
For example: execute a trade to purchase 100 shares of a stock once all criteria for execution are positive and exit the trade given another set of criteria over the coming months or years.
Financial & Professional Services
Asset Management ( Algo Trading )
Specializing in solving business problems through Data Management, Business Intelligence and Analytics. Providing clients across a range of sectors with end to end data solutions. Helping assess, develop, support and deliver complex data programs.
Teradata, Python, R, VBA and Cloud computing