Senior Data Engineer

Reposted 12 Days Ago
3 Locations
In-Office
128K-182K Annually
Senior level
Big Data • Fintech • Mobile • Payments • Financial Services • Data Privacy
The Role
Leads enterprise data engineering and AI-ready data knowledge initiatives across Network Services. Designs scalable pipelines, data models, metadata, governance, lineage, quality controls, and validation frameworks for structured and unstructured data. Partners with subject matter experts to prepare trusted context for AI and agentic workflows, guides hybrid data architectures, supports testing and releases, establishes control standards, and mentors data engineers.
Summary Generated by Built In

Job Description:

At 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!
 

Job Description:

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.

Position Summary:

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.

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.

Required Qualifications:

  • 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

Desired Qualifications:

  • 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.

Skills:

  • Analytical Thinking
  • Application Development
  • Data Management
  • Risk Management
  • Solution Design
  • Agile Practices
  • Architecture
  • Collaboration
  • Decision Making
  • DevOps Practices
  • Data Quality Management
  • Python
  • SQL
  • Critical Thinking

Shift:

1st shift (United States of America)

Hours Per Week: 

40

Pay Transparency details

US - NJ - Jersey City - 101 Hudson St - 101 Hudson (NJ2101)

Pay and benefits information

Pay range$128,000.00 - $182,300.00 annualized salary, offers to be determined based on experience, education and skill set.

Discretionary incentive eligible

This 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.

Benefits

This 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.

Skills Required

  • Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, Mathematics, Statistics, or a 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
  • Master's degree in a technical or data-related field
  • 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 and on-premises data architectures
  • Background in regulated industries with audit, risk, and compliance requirements
  • Proven track record of driving data strategy and platform modernization initiatives

Bank of America Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Bank of America and has not been reviewed or approved by Bank of America.

  • Healthcare Strength Health coverage is described as comprehensive, with medical, dental, vision, virtual care via Teladoc, wellness programs, and specialized support for cancer and menopause. Wellness credits and an always‑on EAP with in‑person sessions add to the depth of care.
  • Parental & Family Support New parents can access up to 26 weeks of leave, including 16 weeks fully paid for eligible teammates, alongside back‑up child and adult care. Family‑building resources and reimbursements (e.g., fertility, adoption, surrogacy) and a dedicated Life Event Services team extend support across life stages.
  • Equity Value & Accessibility Broad‑based equity through the Sharing Success program, including $1B in stock to nearly all non‑executive employees in January 2026, is intended to foster an ownership mindset. Stock awards (including RSUs) are a recurring component that aligns employees’ interests with shareholders.

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The Company
HQ: Charlotte, NC
208,000 Employees
Year Founded: 1784

What We Do

We make financial lives better for our clients and our communities through the power of every connection. Our employees are at the heart of this purpose, and are key to driving responsible growth. Every day, across the globe, our employees bring a commitment to our purpose and to driving responsible growth by living our values: deliver together, act responsibly, realize the power of our people and trust the team. A key aspect of driving responsible growth is doing so in a sustainable manner, a critical pillar of which is being a great place to work for our teammates.

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