Risk Consulting - Digital Risk - Senior - Dev Sec

What You'll Do

• Designing and implementing solutions to various data related technical/compliance challenges such as DevSecOps, data strategy, data governance, data risks & relevant controls, data testing, data architecture, data platforms, data solution implementation, data quality and data security to manage and mitigate risk.
• Leveraging data analytics tools/software to build robust and scalable solutions through data analysis and data visualizations using SQL, Python and visualization tools
• Design and implement comprehensive data analytics strategies to support business decision-making.
• Collect, clean, and interpret large datasets from multiple sources, ensuring completeness, accuracy and integrity of data.
• Integrating and/or piloting next-generation technologies such as cloud platforms, machine learning and Generative AI (GenAI)
• Developing custom scripts and algorithms to automate data processing and analysis to generate insights
• Applying business / domain knowledge including regulatory requirements and industry standards to solve complex data related challenges
• Analyzing data to uncover trends and generate insights that can inform business decisions
• Build and maintain relationships across Engineering, Product, Operations, Internal Audit, external audit and other external stakeholders to drive effective financial risk management.
• Work with DevSecOps, Security Assurance, Engineering, and Product teams to improve efficiency of control environments and provide risk management through implementation of automation and process improvement
• Bridge gaps between IT controls and business controls, including ITGCs and automated business controls. Work with IA to ensure complete control environment is managed
• Work with emerging products to understand risk profile and ensure an appropriate control environment is established
• Implement new process and controls in response to changes to the business environment, such as new product introduction, changes in accounting standards, internal process changes or reorganization.

What You'll Need

• Experience in data architecture, data management, data engineering, data science or data analytics
• Experience in building analytical queries and dashboards using SQL, noSQL, Python etc.
• Proficient in SQL and quantitative analysis, you can deep dive into large amounts of data, draw meaningful insights, dissect business issues and draw actionable conclusions
• Knowledge of tools in the following areas:
  • Scripting and Programming (e.g., Python, SQL, R, Java, Scala, etc.)
  • Big Data Tools (e.g., Hadoop, Hive, Pig, Impala, Mahout, etc.)
  • Data Management (e.g., Informatica, Collibra, SAP, Oracle, IBM etc.)
  • Predictive Analytics (e.g., Python, IBM SPSS, SAS Enterprise Miner, RPL, Matl, etc.)
  • Data Visualization (e.g., Tableau, PowerBI, TIBCO-Spotfire, CliqView, SPSS, etc.)
  • Data Mining (e.g., Microsoft SQL Server, etc.)
  • Cloud Platforms (e.g., AWS, Azure, or Google Cloud)
• Ability to analyze complex processes to identify potential financial, operational, systems and compliance risks across major finance cycles
• Ability to assist management with the integration of security practices in the product development lifecycle (DevSecOps)
• Experience with homegrown applications in a microservices/dev-ops environment
• Experience with identifying potential security risks in platform environments and developing strategies to mitigate them
• Experience with SOX readiness assessments and control implementation
• Knowledge of DevOps practices, CI/CD pipelines, code management and automation tools (e.g., Jenkins, Git, Phab, Artifactory, SonarQube, Selenium, Fortify, Acunetix, Prisma Cloud)

Preferred:

• Experience in:
  • Managing technical data projects
  • Leveraging data analytics tools/software to develop solutions and scripts
  • Developing statistical model tools and techniques
  • Developing and executing data governance frameworks or operating models
  • Identifying data risks and designing and/or implementing appropriate controls
  • Implementation of data quality process
  • Developing data services and solutions in a cloud environment
  • Designing data architecture
  • Analyzing complex data sets & communicating findings effectively
• Process management experience, including process redesign and optimization
• Experience in scripting languages (e.g., Python, Bash)
• Experience in cloud p