Position Summary:Join a groundbreaking team at Bank of America, at the forefront of innovation in AI. We are building the next generation of Gen AI platform, empowering new AI initiatives across Consumer, Small Business, Global Banking, and Wealth organizations. This is a unique opportunity to contribute to a critical platform that will enable secure, scalable, and high-performance AI capabilities across the organization. We value curiosity, collaboration, and a passion for pushing the boundaries of what's possible with AI.
This position is focused on design, build, and serve the Gen AI BI capabilities.
his is a unique opportunity to shape the enterprise data ecosystem by establishing governance standards, improving data discoverability, enabling self-service capabilities, supporting platform tenants, and ensuring secure, scalable, and compliant usage of data platform technologies. The ideal candidate combines deep expertise in data governance and metadata management with a strong customer-centric mindset focused on platform adoption and business value realization.
This role is responsible for defining and leading the governance, adoption, onboarding, and tenant support strategy for enterprise data platforms while partnering with engineering, architecture, risk, and business stakeholders to drive enterprise-wide outcomes.This job is responsible for driving data engineering efforts to deliver enterprise-wide capabilities and complex data solutions. Key responsibilities include directing code design and delivery tasks associated with the integration, cleaning, transformation, and control of data in operational and analytical data systems and working with the Project Management team to define outcomes and inform work structures. Job expectations include providing technical thought leadership by implementing complex data solutions and interactions across multiple systems and domains.
Responsibilities:- Assembles large, complex data sets that meet functional and non-functional requirements, ensuring that the design and engineering approach is consistent across multiple systems
- Maintains, improves, cleans, and manipulates large data for operational and analytics data systems, builds complex processes supporting data transformation, data structures, metadata, data quality controls, dependency, and workload management, and communicates required information for deployment, maintenance, and support of business functionality
- Utilizes multiple architectural components in the design and development of client requirements and collaborates with development teams to understand data requirements and ensure the data architecture is feasible to implement
- Defines and builds data pipelines to enable data-informed decision making, ensuring adherence to release processes and risk management routines
- Contributes to existing test suites including integration, regression, and performance, analyzes test reports, identifies any test issues and errors, and leads triage of underlying causes
- Leads the identification of gaps in data management standards adherence and works with appropriate partners to develop plans to close gaps, leading concept testing and conducting research to prototype toolsets and improve existing processes
- Mentors Data Engineers in the delivery and release of continuous integration and continuous delivery events and defines key performance indicators and internal controls
- Define and execute strategic roadmaps for governance, adoption, and tenant service capabilities.
- Partner with engineering organizations to ensure platform services are scalable, secure, reliable, and aligned with enterprise standards.
- Establish operating models, service processes, SLAs, and support frameworks that improve platform stability and user experience.
- Evaluate emerging technologies and industry best practices related to data governance, cataloging, metadata management, platform operations, and AI governance.
- Mentor and coach team members while fostering a culture of governance, accountability, innovation, and customer-focused delivery.
- Lead the implementation and continuous improvement of enterprise data governance frameworks, standards, controls, and operating models.
- Drive adoption of metadata management, data lineage, business glossaries, data classification, and data quality practices across the enterprise.
- Partner with data stewards, platform teams, architects, and risk partners to ensure compliance with governance, privacy, security, and regulatory requirements.
- Identify governance gaps and establish remediation plans to improve data quality, transparency, accountability, and trust.
- Enable effective discovery, understanding, and consumption of enterprise data assets through governance-enabled self-service capabilities.
- Promote consistent use of lineage, classification, tagging, annotations, and data quality indicators to improve trust and usability.
- Develop and execute strategies to increase adoption of enterprise data platform capabilities, self-service analytics, and AI-enabled data solutions.
- Define onboarding frameworks, best practices, playbooks, and training programs to accelerate tenant success and platform utilization.
- Partner with business and technology stakeholders to understand user needs and improve platform capabilities, experience, and accessibility.
- Drive awareness and education programs to increase understanding of platform services, governance requirements, and supported use cases.
- Measure adoption trends, identify barriers, and implement continuous improvement initiatives to maximize business value.
Required qualifications:- 10+ years of experience leading data management, data analytics, data engineering, data governance, or data platform initiatives enabling AI, or GenAI.
- Deep expertise in data governance, metadata management, data lineage, business glossaries, data quality, master data management, and information lifecycle management.
- Strong experience implementing governance controls supporting privacy, security, risk, and regulatory compliance requirements.
- Proven success driving enterprise adoption of data platforms, self-service analytics, cloud data services, and AI-enabled solutions.
- Hands-on experience with metadata and governance platforms, including data catalogs, lineage tools, semantic layers, knowledge graphs, and business metadata repositories.
- Experience establishing operating models for platform onboarding, tenant enablement, customer success, and service delivery.
- Strong understanding of modern cloud-based data platforms and large-scale enterprise data ecosystems.
- Experience managing multiple business tenants in shared enterprise platforms while ensuring appropriate governance, security, and operational controls.
- Ability to define and monitor platform adoption metrics, governance KPIs, service health metrics, and operational effectiveness measures.
- Excellent stakeholder management, communication, and influencing skills with senior business and technology leadership.
- Demonstrated ability to balance strategic planning with hands-on execution in a complex enterprise environment.
Desired Qualifications- Experience supporting enterprise AI, analytics, or data science platforms from a governance and enablement perspective.
- Knowledge of data governance frameworks, AI governance, responsible AI, and model lifecycle governance.
- Experience leading cloud migration, platform modernization, and adoption programs.
- Strong understanding of user experience and customer journey design for enterprise data platforms.
- Experience developing training, enablement, and community engagement programs to drive sustained platform adoption.
- Track record of building high-performing teams and driving a culture of quality, innovation, accountability, and continuous improvement.
- Experience evaluating and piloting emerging governance, cataloging, observability, and platform management technologies.
Skills:- Analytical Thinking
- Application Development
- Data Management
- Risk Management
- Solution Design
- Agile Practices
- Architecture
- Collaboration
- Decision Making
- DevOps Practices
- Business Acumen
- Data Quality Management
- Financial Management
- Solution Delivery Process
- Test Engineering
Shift:1st shift (United States of America)
Hours Per Week: 40
