Data Engineer III (AI/BI)

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. This 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 efforts to develop and deliver complex data solutions to accomplish technology and business goals. Key responsibilities include leading code design and delivery tasks with the integration, cleaning, transformation and control of data in operational and analytical data systems. Job expectations include liaising with vendors and working with stakeholders and Product and Software Engineering teams to implement data requirements, analyzing performance, and researching and troubleshooting issues within system engineering domains., * Leads story refinement and delivery of requirements through the delivery lifecycle and assists team members in resolving technical complexities

• Codes complex solutions to integrate, clean, transform, and control data, builds processes supporting data transformation, data structures, metadata, data quality controls, dependency, and workload management, assembles complex data sets, and communicates required information for deployment
• Leads documentation of system requirements, collaborates with development teams to understand data requirements and feasibility, and leverages architectural components to develop client requirements
• Leads testing teams to develop test plans, contributes to existing test suites including integration, regression, and performance, analyzes test reports, identifies test issues and errors, and leads triage of underlying causes
• Leads work efforts with technology partners and stakeholders to close gaps in data management standards adherence, negotiates paths forward by thinking outside the box to identify and communicate solutions to complex problems, and leverages knowledge of information systems, techniques, and processes
• Leads complex information technology projects to ensure on-time delivery and adherence to release processes and risk management and defines and builds data pipelines to enable data-informed decision making
• Mentors Data Engineers to enable continuous development and monitors 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.
• 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 gaps and establish remediation plans to improve data quality, transparency, accountability, and trust.
• Enable effective 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., Bank of America and its affiliates consider for employment and hire qualified candidates without regard to race, religious creed, religion, color, sex, sexual orientation, genetic information, gender, gender identity, gender expression, age, national origin, ancestry, citizenship, protected veteran or disability status or any factor prohibited by law, and as such affirms in policy and practice to support and promote the concept of equal employment opportunity, in accordance with all applicable federal, state, provincial and municipal laws. The company also prohibits discrimination on other bases such as medical condition, marital status or any other factor that is irrelevant to the performance of our teammates.

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• 8+ years of experience leading data management, data analytics, data engineering, data governance, or data platform initiatives enabling AI, or GenBI.
• 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., * 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
• DevOps Practices
• Solution Design
• Agile Practices
• Collaboration
• Decision Making
• Risk Management
• Test Engineering
• Architecture
• Business Acumen
• Data Quality Management
• Financial Management
• Solution Delivery Process

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