Data Analytics and Operations Specialist - Internal Audit

Group Details

We are seeking a remote Full-Stack Data Systems Engineer to lead the deployment and operationalization of AI/ML models within a scalable audit software platform. This hybrid role blends architectural vision with hands-on engineering, focusing on transforming audit prototypes into reusable, production-grade applications. Key responsibilities include designing and maintaining robust data pipelines, managing model deployment workflows, and ensuring system reliability through CI/CD, monitoring, and automation. The engineer will collaborate closely with audit experts to automate repetitive tasks, enforce software reusability, and drive operational excellence. This role offers strategic influence over system architecture while remaining deeply engaged in the technical execution of data, ML, and infrastructure components.

Success will be measured by time to market, stability, scalability, usability, and versatility of the data analytics platform, which is designed to enhance the efficiency and effectiveness of business and technology audits. The role will involve the development and implementation of advanced data solutions that will increase the efficiency and effectiveness of audits and enable continuous auditing and risk monitoring of key risk areas.

Job Responsibilities

• Support the Director of Data Analytics in building the Data Analytics, Internal Audit team
• Regional representation of the Data Analytics, Internal Audit team
• Drive the overall system architecture for a scalable audit automation and AI platform
• Help inform Buy vs Build decisions
• Convert audit prototypes into reusable, production-grade software components.
• Build and maintain robust data pipelines and AI/ML workflows to support audit analytics and reporting.
• Deploy and operationalize machine learning models with a focus on reliability, performance, and maintainability.
• Ensure operational stability through CI/CD automation, system monitoring, and proactive troubleshooting.
• Drive best practices in software development, testing, deployment, and documentation.
• Collaborate with the audit teams and other Data analytics team members to translate domain needs into technical solutions.
• Enforce software modularity and reusability to support incremental platform growth.
• Understand and leverage Tradewebs technology stack to maximize integration and reduce the platform technology risk/debt.
• Apply a pragmatic, ROI-driven approach to technology selectionleveraging advanced solutions only when justified.
• Contribute to continuous auditing and risk monitoring capabilities by integrating quasi real-time data flows.
• Maintain and optimize database systems (SQL/NoSQL) for secure and efficient audit data storage and retrieval.
• Document system designs, deployment processes, and operational procedures for long-term sustainability.
• Support platform usability and versatility to enhance audit coverage and strengthen the organizations control posture.

Qualifications

• 10+ years of proven experience in cross-regional collaboration in the financial industry
• Excellent communication skills and a high degree of autonomy, with the ability to collaborate effectively both the local team and with a core team based in New York and operate seamlessly across time zones.
• Comfortable working in a remote, globally distributed environment with accountability and initiative.
• Track record in attracting, training and retaining talents
• Ability to translate business requirements into robust technical solutions through close collaboration with domain experts.
• Strong background in system architecture and software engineering, with a track record of designing scalable platforms for data-driven applications.
• Proficiency in building and maintaining data pipelines, deploying machine learning models, and managing end-to-end ML Ops workflows.
• Deep understanding of CI/CD practices, infrastructure automation, system monitoring, and troubleshooting in production environments.
• Expertise in software development best practices, including modular design, code reusability, testing, and documentation.
• Hands-on experience with SQL and NoSQL databases, data modeling, and performance optimization for audit and analytics use cases.
• Demonstrated success in applying a pragmatic, ROI-driven approach to technology adoptionprioritizing simplicity and impact over novelty.
• Knowledge of continuous auditing and risk monitoring concepts, with experience integrating real-time or near-real-time data flows.
• Wide range of technology exposure including:
• multiple programming language : Python, C++, Javascript, VBA
• available data tools for visualization Tableau, PowerBi, HTML
• available AI/Ml platform : KNIME, ALTERYX, others
• code and no code development tools.