VP/Director, Global Markets Data Incident Management

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Global Markets Data Management is responsible for establishing and executing data management strategy and data governance for GM. The team works closely with other groups within GM, other lines of business and Enterprise functions. The team drives strategic initiatives and projects in the areas of data management and governance.

The GM Data Incident Management team within GMDM is accountable for remediation of data incidents concerning consumption of GM Data. The team triages, prioritizes and remediates GM data incidents, working closely with other groups within GM and other lines of business, including Technology, support partners (in particular Market Risk and Counterparty Credit Risk) and Enterprise functions at all levels.

The ideal candidate will have expert knowledge of GM products, data structures, processes, strong project management skills, and experience in running data management projects, and understanding Bank of America’s policies, standards, and procedures.

Responsibilities:

• Manage data incident triage with key stakeholders.
• Act as a liaison between technical and non-technical stakeholders, translating complex data concepts into actionable insights.
• Provide SME knowledge and interface with various technology, quantitative, and business groups, representing providers and consumers of GM’s data.
• Deliver insights from large datasets, design data solutions, and communicate solutions to stakeholders.
• Strong ability to conceptualize business requirements as functions and data models.
• Effectively manages multiple concurrent initiatives and competing priorities, consistently delivering high-quality outcomes in a fast-paced and complex environment.
• Demonstrates exceptional organizational and execution skills, balancing strategic objectives, operational responsibilities, and time-sensitive deliverables while maintaining a high standard of accuracy and accountability.
• Conducts comprehensive analyses of complex data quality issues, performing detailed root-cause investigations to identify underlying drivers, assess business impact, and develop sustainable remediation strategies.
• Provides leadership in the identification, prioritization, and resolution of data quality issues, driving cross-functional collaboration, stakeholder alignment, and timely execution of corrective actions.
• Develops automated reporting and monitoring solutions using Python, SQL, and HUE/Oozie workflows to improve efficiency, scalability, and data transparency.
• Influences and partners effectively with business, operations, technology, and data stakeholders to advance remediation efforts, strengthen controls, and improve data quality outcomes.
• Exercises sound judgment in evaluating risks, escalating issues when appropriate, and ensuring that data-related challenges are addressed in a disciplined and transparent manner.

Skills:

• High intensity and initiative – thrives in a fast-paced, high-stakes environment.
• Curious and analytical mindset – proactive in exploring new ways to improve and optimize data processes.
• Strong analytical and relentless problem-solving skills, critical thinking.
• In depth knowledge of one or more areas within GM or related functions.
• Knowledge of financial markets, traded products, trade life cycle management and market conventions.
• Ability to facilitate discussions across various levels of stakeholders.
• Ability to multitask and properly prioritize multiple projects.
• Good communication, interpersonal, and organizational skills.
• Proficiency in Python (Pandas, NumPy) for data processing and automation.
• Strong SQL skills, with experience in relational databases (PostgreSQL, SQL Server, Oracle) and NoSQL databases.

Desired Skills/Requirements:

• Bachelor's Degree
• Knowledge of the various functions within GM trading organization and other functions interacting with GM, such as Operations, Market Risk, Counterparty Credit Risk Finance.
• Knowledge of General Ledger and Chart of Accounts.
• Knowledge / understanding of trading agreements.
• Knowledge of big data frameworks (Hadoop, Spark, Kafka).
• Understanding of API integrations, data lakes, and real-time data streaming architectures.

Shift:

1st shift (United States of America)

Hours Per Week: 

40