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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 and providing a culture of caring is core to how we drive Responsible Growth. We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed, build a career and contribute to our shared success. This includes attracting and developing exceptional talent, recognizing and rewarding performance, and supporting our teammates’ physical, emotional, and financial wellness through affordable, competitive and flexible benefits.
We value the unique perspectives individuals bring from all backgrounds and career paths \- whether shaped by military service, community college education, or a wide range of work and life experiences. These journeys foster resilience, leadership and innovation, strengthening our workforce and positively impact the communities we serve.
Bank of America is committed to an in\-office culture that supports collaboration, engagement, and career development. Our approach includes clear in\-office expectations, while providing an appropriate level of flexibility based on role\-specific responsibilities and business needs.
At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!
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
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 4,317 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Bank of America, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills in Demand for This Role
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $185,000 based on 83 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Bank of America AI Hiring
Bank of America has 6 open AI roles right now. They're hiring across Data Engineer, AI Software Engineer, AI/ML Engineer. Positions span New York, NY, US, Addison, TX, US, Charlotte, NC, US. Compensation range: $182K - $230K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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