Senior Data Engineer

Bank of America•Jersey City, NJ

1d•Onsite

About The Position

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. This 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.

Requirements

Bachelor’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 processing
Strong experience implementing data quality frameworks and automated validation
Advanced knowledge of data modeling, metadata, and lifecycle management
Experience with data governance, lineage, and control frameworks
Strong understanding of AI/ML data requirements and data readiness practices
Proven ability to design solutions for scalable, high-performance analytics platforms

Nice To Haves

Master's Degree in a technical or data-related field is a plus
Experience enabling AI-ready data ecosystems and advanced analytics platforms
Demonstrated success implementing enterprise data quality programs and governance frameworks
Strong knowledge of DataOps and MLOps practices
Experience designing and supporting hybrid (cloud + on-prem) data architectures
Background in regulated industries with audit, risk, and compliance requirements
Proven track record of driving data strategy and platform modernization initiatives
Combines 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.

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
Lead 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.