Data Management: Design, develop, and maintain the data infrastructure that supports the Finance systems, ensuring data accuracy, integrity, and availability.
Data Modeling: Apply data modeling techniques to design efficient and scalable data structures that support the Finance systems' reporting and analysis requirements.
ETL Development: Utilize ETL tools like Informatica or extract, transform, and load data from various sources into the Finance systems, ensuring smooth and accurate data flow.
Finance System Management: Collaborate with Finance stakeholders to understand their data requirements and ensure that the data infrastructure meets their needs for financial consolidation, regulatory reporting, cost allocation, and profitability reporting.
Communication and Stakeholder Management: Strong communication skills to interact with various stakeholders, including senior management, technical teams, and business users.
Data Mapping: Perform data mapping exercises to align data from diverse sources to the standardized OFSAA data model, ensuring data consistency and accuracy.
Data Quality and Governance: Implement data quality and governance processes to maintain high data quality standards across the Finance systems.
Performance Optimization: Identify and implement performance optimization techniques to ensure efficient data processing and reporting within the Finance systems.
Integration: Collaborate with IT teams and external vendors to integrate new data sources and applications with the Finance systems.
Regulatory Compliance: Thorough understanding of regulatory reporting requirements and compliance standards in the banking industry.
AI/ML: Apply AI/ML for data classification, metadata inference, automated test‑case/document generation, and reconciliation efficiency.
Agile Delivery: Works as part of cross‑functional Agile squads, contributing to sprint planning, refinement, and iterative delivery of data pipelines and finance systems enhancements.
Requirements:
Minimum required experience is 5 years.
Bachelor's degree in computer science, Information Technology, or a related field.
Proven experience as a Data Engineer, handling Finance systems like OFSAA, Financial Consolidation, and Regulatory Reporting.
Expertise in data modeling techniques and ETL tools (e.g., Informatica) to manage data integration and transformation processes.
Knowledge of banking finance functions, including financial consolidation, cost allocation, and profitability reporting.
Familiarity with big data technologies and their applications in data management and analysis.
Understanding Hadoop architecture, tools like HDFS, Hive, Sqoop, Spark is critical for the tool.
Proficiency in SQL and database management systems.
Analytical mindset with a focus on data accuracy and attention to detail.
Strong problem-solving skills and the ability to work independently as well as in a team environment.
Strong knowledge of Agile delivery in banking/financial services.
Proficiency with delivery/collaboration tooling (Jira/Azure DevOps, Confluence, Miro) and automated reporting (Power BI, Tableau).
Familiarity with AI/ML concepts (basic model lifecycle, data quality, explainability, monitoring) and responsible AI considerations applied to data engineering.
Awareness of data governance, privacy, cybersecurity, and regulatory expectations relevant to finance and regulatory data.
