Senior Data Engineer

Data Engineer Role at Bank of AmericaAt Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities, and shareholders every day. Being a Great Place to Work is core to how we drive Responsible Growth. This includes our commitment to being an inclusive workplace, attracting and developing exceptional talent, supporting our teammates' physical, emotional, and financial wellness, recognizing and rewarding performance, and how we make an impact in the communities we serve.Bank of America is committed to an in-office culture with specific requirements for office-based attendance and which allows for an appropriate level of flexibility for our teammates and businesses based on role-specific considerations. At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!Position SummaryThis role is responsible for leading the design, governance, and continuous improvement of the data and knowledge foundations that support AI and agentic capabilities across Network Services. The individual in this role works across both structured and unstructured data sources to ensure information is defined, organized, governed, and made available in a manner suitable for trusted AI consumption. Within the Data Knowledge pillar, this role provides senior-level leadership in defining standards, guidelines, and guardrails for how operational knowledge, documentation, configurations, telemetry, metadata, and other contextual assets are curated, controlled, and prepared for model use. This individual partners closely with product and service subject matter experts to understand network technologies, operational workflows, and domain context so that high-quality data and knowledge can be translated into reusable AI-ready assets. Compared with Data Engineer III, this role carries broader accountability for setting direction, influencing standards, designing scalable control frameworks, and guiding more complex cross-domain data and knowledge initiatives that improve the quality, trustworthiness, and operational supportability of context provided to models.ResponsibilitiesAssembles large, complex data sets that meet functional and non-functional requirements, ensuring that the design and engineering approach is consistent across multiple systemsMaintains, 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 functionalityUtilizes 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 implementDefines and builds data pipelines to enable data-informed decision making, ensuring adherence to release processes and risk management routinesContributes to existing test suites including integration, regression, and performance, analyzes test reports, identifies any test issues and errors, and leads triage of underlying causesLeads 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 processesMentors Data Engineers in the delivery and release of continuous integration and continuous delivery events and defines key performance indicators and internal controlsLead the design and improvement of data and knowledge assets that support AI and agentic use-cases across Network Services, including both structured and unstructured sources.Define and maintain standards for how documentation, configurations, telemetry, metadata, policies, standards, and operational knowledge should be organized, governed, and prepared for AI consumption.Partner with product and service subject matter experts to understand network technologies, operational context, and domain-specific knowledge required to improve model grounding and decision quality.Design and guide scalable methods for storing, governing, indexing, validating, and retrieving context assets needed for AI-enabled workflows and solutions.Establish and evolve preventative and detective controls that identify and reduce data quality, metadata quality, knowledge quality, lineage, and freshness issues before they affect downstream AI use.Define expectations for ownership, stewardship, lineage, freshness, governance, and control accountability across relevant data and knowledge domains.Build or guide the development of pipelines, transformations, validation routines, metadata structures, and supporting services that improve the quality and accessibility of AI-relevant context assets.Required QualificationsBachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, Mathematics, Statistics, or related field.7+ years of experience in data engineering, with demonstrated leadership in enterprise data platforms.Advanced proficiency in SQL and Python.Deep expertise in data integration, pipeline development, and large-scale data processingStrong experience implementing data quality frameworks and automated validationAdvanced knowledge of data modeling, metadata, and lifecycle managementExperience with data governance, lineage, and control frameworksStrong understanding of AI/ML data requirements and data readiness practicesProven ability to design solutions for scalable, high-performance analytics platformsDesired QualificationsMaster's Degree in a technical or data-related field is a plusExperience enabling AI-ready data ecosystems and advanced analytics platformsDemonstrated success implementing enterprise data quality programs and governance frameworksStrong knowledge of DataOps and MLOps practices Experience designing and supporting hybrid (cloud + on-prem) data architecturesBackground in regulated industries with audit, risk, and compliance requirementsProven track record of driving data strategy and platform modernization initiativesCombines deep expertise in data integration, quality, modeling, and governance to build scalable, controlled, and fully traceable data platforms that enable AI-ready and reliable analytics outcomes.SkillsAnalytical ThinkingApplication DevelopmentData ManagementRisk ManagementSolution DesignAgile PracticesArchitectureCollaborationDecision MakingDevOps PracticesData Quality ManagementPythonSQLCritical ThinkingShift1st shift (United States of America)Hours Per Week40Pay Transparency DetailsUS - NJ - Jersey City - 101 Hudson St - 101 Hudson (NJ2101)Pay and benefits informationPay range$128,000.00 - $182,300.00 annualized salary, offers to be determined based on experience, education, and skill set. Discretionary incentive eligibleThis role is eligible to participate in the annual discretionary plan. Employees are eligible for an annual discretionary award based on their overall individual performance results and behaviors, the performance and contributions of their line of business and/or group; and the overall success of the Company.BenefitsThis role is currently benefits eligible. We provide industry-leading benefits, access to paid time off, resources and support to our employees so they can make a genuine impact and contribute to the sustainable growth of our business and the communities we serve.