Data Analyst - Audit

Role: Data Analyst - Min - 6 years of Exp Key Responsibilities: 1. Strategic Leadership & Roadmap Development Define the Vision: Design and execute the long-term data analytics strategy, for proactive Continuous Monitoring . Methodology Evolution: Lead the transition toward full-population testing by embedding analytics into the risk assessment phase. Governance Framework: Strengthen data governance framework to ensure the integrity, security, and accuracy of data used in audit reporting, ensuring all workflows are documented for regulatory reliance. 2. Advanced Analytics & Tech Stack (Python, SQL, Power BI) Advanced Modeling (Python): Oversee the development of complex models using Python or R . Utilize predictive analytics, statistical sampling, and machine learning algorithms to anticipate emerging risks rather than just reporting on past events. Data Extraction & Manipulation (SQL): leverage advanced SQL scripting to query enterprise data warehouses directly. Visualization & Storytelling (Power BI): Architect dynamic executive dashboards using Power BI that translate complex datasets into intuitive visual stories, allowing stakeholders to "self-serve" risk insights. Fraud Detection: Architect sophisticated fraud detection scenarios and behavioral analysis models to identify anomalies across financial and operational datasets. 3. Stakeholder Management & Business Impact Translating Data to Value: Act as the bridge between technical data teams and business stakeholders. You must articulate complex findings into clear, actionable business insights for the Audit Committee. Business Acumen: Apply deep business knowledge to analytics. You must understand how the business generates revenue and where operational risks lie to ensure models are commercially relevant, not just theoretically correct. Cross-Functional Collaboration: Partner with internal teams to leverage existing data lakes and align on architecture. 4. Team Leadership & Quality Assurance Mentorship: Manage and mentor a team of data analysts and auditors. Foster their technical growth in SQL querying, Python scripting, and Power BI dashboarding. Quality Control: Ensure all analytical deliverables meet rigorous documentation standards. Validate code logic and query integrity to ensure results are accurate and suitable for external audit reliance. Role: Data Analyst - Min - 6 years of Exp Key Responsibilities: 1. Strategic Leadership & Roadmap Development Define the Vision: Design and execute the long-term data analytics strategy, for proactive Continuous Monitoring . Methodology Evolution: Lead the transition toward full-population testing by embedding analytics into the risk assessment phase. Governance Framework: Strengthen data governance framework to ensure the integrity, security, and accuracy of data used in audit reporting, ensuring all workflows are documented for regulatory reliance. 2. Advanced Analytics & Tech Stack (Python, SQL, Power BI) Advanced Modeling (Python): Oversee the development of complex models using Python or R . Utilize predictive analytics, statistical sampling, and machine learning algorithms to anticipate emerging risks rather than just reporting on past events. Data Extraction & Manipulation (SQL): leverage advanced SQL scripting to query enterprise data warehouses directly. Visualization & Storytelling (Power BI): Architect dynamic executive dashboards using Power BI that translate complex datasets into intuitive visual stories, allowing stakeholders to "self-serve" risk insights. Fraud Detection: Architect sophisticated fraud detection scenarios and behavioral analysis models to identify anomalies across financial and operational datasets. 3. Stakeholder Management & Business Impact Translating Data to Value: Act as the bridge between technical data teams and business stakeholders. You must articulate complex findings into clear, actionable business insights for the Audit Committee. Business Acumen: Apply deep business knowledge to analytics. You must understand how the business generates revenue and where operational risks lie to ensure models are commercially relevant, not just theoretically correct. Cross-Functional Collaboration: Partner with internal teams to leverage existing data lakes and align on architecture. 4. Team Leadership & Quality Assurance Mentorship: Manage and mentor a team of data analysts and auditors. Foster their technical growth in SQL querying, Python scripting, and Power BI dashboarding. Quality Control: Ensure all analytical deliverables meet rigorous documentation standards. Validate code logic and query integrity to ensure results are accurate and suitable for external audit reliance.