Data Architect / Data Engineer - Hybrid

Bank of America is seeking a Data Architect / Data Engineer to develop, govern, and improve data and knowledge foundations supporting AI and agentic capabilities across Network Services. The role prepares structured and unstructured information for trusted AI consumption and establishes standards, controls, and practices for managing these assets.

Responsibilities

• Develop and maintain data and knowledge assets that support AI and agentic use-cases across Network Services, including both structured and unstructured sources
• Define and apply standards for how documentation, configurations, telemetry, metadata, policies, standards, and operational knowledge should be organized 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 implement scalable methods for storing, governing, indexing, and retrieving context assets needed for AI-enabled workflows and solutions
• Establish and improve preventative and detective controls that identify and reduce data quality, metadata quality, knowledge quality, and freshness issues before they affect downstream use
• Contribute to data quality remediation efforts by identifying upstream sources of issues, defining corrective actions, and improving the reliability of data at source
• Define and maintain expectations for data ownership, lineage, freshness, stewardship, and governance across relevant data and knowledge domains
• Build and enhance pipelines, transformations, validation routines, and supporting services that improve the quality and accessibility of data and knowledge assets
• Support the creation of reusable templates, patterns, and guidance for knowledge artifacts, documentation standards, and context management practices
• Work across engineering, architecture, operations, and governance teams to ensure data and knowledge practices align with enterprise controls, delivery needs, and approved standards
• Monitor and communicate the health, readiness, and quality of AI-relevant data and knowledge assets, including issues, gaps, and remediation priorities
• Contribute to continuous improvement of data and knowledge management practices that strengthen trust, reuse, traceability, and operational supportability of AI context
• Document data definitions, controls, transformations, and usage considerations so that downstream teams can reliably consume and support the resulting assets

Skills

• Experience engineering and managing both structured and unstructured data used for analytics, automation, search, or AI-enabled solutions
• Strong understanding of data modeling, transformation, storage, and retrieval patterns needed to support scalable and governed data pipelines
• Ability to define and apply standards for how data, documents, knowledge artifacts, metadata, and operational context should be prepared for AI consumption
• Experience establishing data quality controls, validation checks, and monitoring approaches that improve trust, completeness, consistency, and usability of data assets
• Knowledge of preventative and detective controls used to identify, prevent, and remediate data quality, metadata, or knowledge management issues
• Experience working with knowledge sources such as documentation, standards, policies, configurations, runbooks, telemetry, and operational records
• Ability to work with product and service subject matter experts to understand domain context and translate network operational knowledge into usable data and knowledge assets
• Working understanding of network technologies, infrastructure concepts, and operational service models sufficient to interpret domain-specific context and data requirements
• Experience defining data ownership, lineage, freshness, retention, governance, and stewardship expectations in a regulated enterprise environment
• Familiarity with AI and retrieval-oriented data preparation concepts, including grounding, context structuring, metadata quality, and content readiness for model use
• Ability to design and implement scalable approaches for storing, governing, indexing, and retrieving data and knowledge assets for downstream use
• Strong analytical and problem-solving skills, including the ability to identify upstream causes of data issues and drive sustainable remediation
• Experience working across engineering, architecture, operations, and governance stakeholders to align data practices to enterprise standards and delivery needs
• Strong written and verbal communication skills with the ability to document standards, controls, definitions, and usage guidance clearly
• Experience working in a fast-paced and complex environment with evolving priorities, incomplete data, and cross-functional dependencies
• Strong organizational discipline and attention to detail in support of trusted, production-grade data and knowledge practices

Benefits

• For contract roles: Access to hundreds of clients, most who have been working with Genesis10 for 5-20+ years.
• For contract roles: The opportunity to have a career-home in Genesis10; many of our consultants have been working exclusively with Genesis10 for years.
• For contract roles: Access to an experienced, caring recruiting team (more than 7 years of experience, on average.)
• For contract roles: Behavioral Health Platform
• For contract roles: Medical, Dental, Vision
• For contract roles: Health Savings Account
• For contract roles: Voluntary Hospital Indemnity (Critical Illness & Accident)
• For contract roles: Voluntary Term Life Insurance
• For contract roles: 401K
• For contract roles: Sick Pay (for applicable states/municipalities)
• For contract roles: Commuter Benefits (Dallas, NYC, SF, and Illinois)

Company Overview

Company H1B Sponsorship