Job
Description
You will be leading a global team to ensure the accuracy, completeness, and transparency of reference and transactional data supporting Transaction Lifecycle Management at a leading Global Asset Manager. Your responsibilities will include: - Managing and remediating data quality issues across middle office platforms, ensuring accuracy, timeliness, and completeness of reference and transactional data. - Developing tools for the operations team using industry-leading technologies like Alteryx, PowerBI, GenAI, etc., to pursue automation and enhance efficiency. - Maintaining robust data validation and exception processes with clear monitoring and escalation. - Overseeing input/output data flows across platforms, collaborating with architecture, design, and control teams to support front-to-back transaction lifecycle processes. - Developing and executing functional and outcome-focused data strategies, ensuring data solutions are fit for purpose and aligned with business needs. - Providing full transparency into operational data quality, including readiness for downstream use, by implementing tiered monitoring and exception reporting. - Enabling high-touch data solutions to address complex requirements, supporting the build of more data-enabled workflows and active monitoring. - Driving an end-to-end product mindset across the team, ensuring data management processes support seamless trade lifecycle execution. - Proactively identifying inefficiencies, re-engineering workflows, and applying AI/automation to streamline controls and reduce manual intervention. - Developing and operationalizing data quality controls and embedding them into production workflows. - Partnering with the enterprise Data Strategy & Governance team to ensure global alignment with data standards, frameworks, and policies. - Leading, mentoring, and growing a global team, fostering a culture of accountability, innovation, and continuous improvement. Qualifications required for this role: - 8+ years of experience in Data Engineering, Data Quality, or Transaction Lifecycle Operations within Asset Management, Investment Banking, or related financial institutions. - Strong expertise in middle office data processes, including trade, reference, and transactional data management. - Proven ability to lead global teams and build a culture of operational excellence and innovation. - Experience with data engineering tools, data quality platforms, and reconciliation utilities. - Understanding of enterprise data management principles, governance, and regulatory standards. - Strong problem-solving and analytical skills, with the ability to identify root causes and design sustainable solutions. - Demonstrated track record of applying automation, AI, or machine learning in data workflows. - Excellent communication skills with the ability to collaborate across Technology, Operations, and Data Governance. - Bachelors or Masters degree in Computer Science, Engineering, Finance, or related field. Morgan Stanley, a global leader in financial services since 1935, is committed to maintaining excellence and diversity in serving clients and communities worldwide. They offer opportunities to work alongside a diverse and talented workforce, providing attractive employee benefits and perks. Morgan Stanley is an equal opportunities employer, fostering a culture of inclusion and supporting individuals to maximize their full potential.,
