Engineer 6, Machine Learning, Data & AI

Comcast • Washington, DC

19h • $224,190 - $351,571 • Onsite

About The Position

Make your mark at Comcast -- a Fortune 30 global media and technology company. From the connectivity and platforms we provide, to the content and experiences we create, we reach hundreds of millions of customers, viewers, and guests worldwide. Become part of our award-winning technology team that turns big ideas into cutting-edge products, platforms, and solutions that our customers love. We create space to innovate, and we recognize, reward, and invest in your ideas, while ensuring you can proudly bring your authentic self to the workplace. Join us. You’ll do the best work of your career right here at Comcast. (In most cases, Comcast prefers to have employees on-site collaborating unless the team has been designated as virtual due to the nature of their work. If a position is listed with both office locations and virtual offerings, Comcast may be willing to consider candidates who live greater than 100 miles from the office for the remote option.)
Job Summary
We are seeking a visionary Senior Technologist, Data & Agentic AI Enablement to shape the future of our enterprise data ecosystem and accelerate the next generation of AI-driven experiences. This enterprise leadership role is responsible for two complementary missions:
Preparing enterprise data for an Agentic AI future, ensuring data is trusted, discoverable, semantically rich, governed, and consumable by AI agents. Transforming data engineering through Agentic AI, embedding AI across the data engineering lifecycle to improve productivity, quality, reliability, and speed of delivery. The successful candidate will define the architecture, standards, and engineering patterns that enable AI to become an active participant in the design, development, testing, operation, and optimization of enterprise data platforms. This highly influential role partners closely with Data Engineering, Data Platforms, Enterprise Architecture, Security, Product, and AI teams to accelerate data modernization and AI-enabled business transformation.
Job Description
Agentic AI Transformation of Data Engineering:
Lead the strategy for integrating Agentic AI across the enterprise data engineering lifecycle, enabling AI-assisted development, operations, and platform management.
Responsibilities include:
Define the enterprise roadmap for incorporating AI agents into data engineering workflows, platform operations, and software delivery.
Partner with platform engineering teams to integrate AI capabilities into CI/CD, Infrastructure-as-Code (IaC), and DevSecOps practices.
Evaluate emerging agent frameworks, copilots, and autonomous engineering platforms for enterprise adoption.
Establish best practices and governance for AI-assisted engineering and operational processes.
Enterprise Data Strategy for AI & Agentic Systems:
Develop and drive the enterprise strategy for preparing data assets to support AI, Generative AI, and Agentic AI use cases.
Responsibilities include:
Define principles and reference architectures that enable AI agents to discover, access, understand, and act upon enterprise data safely and effectively.
Partner with business and technology leaders to identify high-value opportunities for Agentic AI solutions.
Establish enterprise standards that align data, AI, and business strategies.
Data Architecture & AI Readiness
Lead architectural efforts to ensure enterprise data is optimized for both human and AI consumption.
Responsibilities include:
Establish standards that ensure data is:
Discoverable
Well-described and semantically rich
Governed and trusted
Accessible through standardized interfaces
Consumable by both people and AI agents
Drive adoption of metadata-driven architectures, semantic models, knowledge graphs, and business ontologies.
Ensure enterprise data products support machine-to-machine interactions in addition to traditional analytics use cases.
Agentic Data Enablement:
Define the frameworks that enable AI agents to effectively interact with enterprise data and knowledge assets.
Responsibilities include:
Establish standards for exposing enterprise data through APIs, semantic layers, data products, and retrieval systems.
Partner with platform teams to develop capabilities supporting:
Retrieval-Augmented Generation (RAG)
Agent orchestration platforms
Tool and API discovery
Vector-based retrieval architectures
Context management and memory frameworks
Develop patterns that allow AI agents to access enterprise knowledge securely and responsibly.
Data Governance & Trust:
Ensure governance and trust frameworks evolve to support autonomous and AI-assisted decision making.
Responsibilities include:
Establish controls for data lineage, provenance, quality, explainability, and auditability.
Partner with Security, Privacy, and Risk teams to implement responsible AI controls and secure data access practices.
Define trust frameworks that enable AI agents to operate within approved business guardrails.
Semantic Layer & Knowledge Management:
Drive the development of enterprise semantic capabilities that improve data accessibility and AI reasoning.
Responsibilities include:
Lead development of enterprise semantic models and shared business definitions.
Improve metadata quality, business context, and knowledge accessibility across the organization.
Advance enterprise knowledge management practices that enhance AI reasoning, discovery, and decision support.
Platform & Ecosystem Alignment:
Collaborate across teams to ensure enterprise platforms support AI-native consumption patterns.
Responsibilities include:
Partner with Data Platform, Engineering, Analytics, and Product teams to align technology roadmaps.
Collaborate with BI and analytics teams to maintain consistent business metrics and semantic definitions across human and AI consumers.
Influence technology investments that enable future AI and Agentic AI capabilities.
Innovation & Thought Leadership
Serve as a strategic thought leader on AI readiness, data modernization, and enterprise architecture.
Responsibilities include:
Monitor emerging trends in AI, Agentic Systems, Data Architecture, and Knowledge Management.
Evaluate innovative technologies and identify strategic adoption opportunities.
Advise executive leadership on enterprise AI strategy and future-state architecture.
Qualifications
+ years of experience in Data Architecture, Data Engineering, Enterprise Architecture, or related technology disciplines.
Proven experience designing and scaling enterprise data ecosystems.
Experience supporting AI, machine learning, advanced analytics, or data modernization initiatives.
Demonstrated success influencing outcomes across large, matrixed organizations.
Technical Expertise
Deep understanding of modern data architectures, including data warehouses, data lakes, lakehouses, and data mesh concepts.
Expertise in metadata management, semantic modeling, data governance, and data product design.
Strong understanding of APIs, event-driven architectures, and interoperability standards.
Experience with AI technologies, including LLMs, RAG architectures, vector databases, agent frameworks, and AI orchestration platforms.
Knowledge of modern software engineering, DevSecOps, CI/CD, and cloud-native architectures.
Leadership & Communication
Strong executive communication and stakeholder management skills.
Ability to translate emerging technologies into practical enterprise strategies.
Proven ability to lead through influence across business and technology organizations.
Demonstrated thought leadership in data, AI, or enterprise architecture domains.
Preferred Candidate Profile :
A strategic technology leader with deep expertise in enterprise data architecture and a passion for advancing AI adoption. This individual combines strong technical vision, architectural leadership, and business acumen to help the organization build a trusted, AI-ready data foundation while transforming the way engineering teams work through Agentic AI.
Disclaimer:
This information has been designed to indicate the general nature and level of work performed by employees in this role. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications.
Skills
Agentic AI, AI Adoption, Data Architecture Development, Data Engineering, Data Strategies, Enterprise Data
Compensation
This job can be performed in Virginia, and District of Columbia with a Pay Range of $224,190.44 - $351,571.37
Comcast intends to offer the selected candidate base pay within this range, dependent on job-related, non-discriminatory factors such as experience. The application window is 30 days from the date job is posted, unless the number of applicants requires it to close sooner or later.
Base pay is one part of the Total Rewards that Comcast provides to compensate and recognize employees for their work. Most sales positions are eligible for a Commission under the terms of an applicable plan, while most non-sales positions are eligible for a Bonus. Additionally, Comcast provides best-in-class Benefits to eligible employees. We believe that benefits should connect you to the support you need when it matters most, and should help you care for those who matter most. That’s why we provide an array of options, expert guidance and always-on tools, that are personalized to meet the needs of your reality – to help support you physically, financially and emotionally through the big milestones and in your everyday life. Please visit the compensation and benefits summary on our careers site for more details.
Education
Bachelor's Degree
While possessing the stated degree is preferred, Comcast also may consider applicants who hold some combination of coursework and experience, or who have extensive related professional experience.
Certifications (if applicable)
Relevant Work Experience
15 Years +
Comcast is an equal opportunity workplace. We will consider all qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, veteran status, genetic information, or any other basis protected by applicable law.

Requirements

• + years of experience in Data Architecture, Data Engineering, Enterprise Architecture, or related technology disciplines.
• Proven experience designing and scaling enterprise data ecosystems.
• Experience supporting AI, machine learning, advanced analytics, or data modernization initiatives.
• Demonstrated success influencing outcomes across large, matrixed organizations.
• Deep understanding of modern data architectures, including data warehouses, data lakes, lakehouses, and data mesh concepts.
• Expertise in metadata management, semantic modeling, data governance, and data product design.
• Strong understanding of APIs, event-driven architectures, and interoperability standards.
• Experience with AI technologies, including LLMs, RAG architectures, vector databases, agent frameworks, and AI orchestration platforms.
• Knowledge of modern software engineering, DevSecOps, CI/CD, and cloud-native architectures.
• Strong executive communication and stakeholder management skills.
• Ability to translate emerging technologies into practical enterprise strategies.
• Proven ability to lead through influence across business and technology organizations.
• Demonstrated thought leadership in data, AI, or enterprise architecture domains.

Nice To Haves

• A strategic technology leader with deep expertise in enterprise data architecture and a passion for advancing AI adoption. This individual combines strong technical vision, architectural leadership, and business acumen to help the organization build a trusted, AI-ready data foundation while transforming the way engineering teams work through Agentic AI.

Responsibilities

• Define the enterprise roadmap for incorporating AI agents into data engineering workflows, platform operations, and software delivery.
• Partner with platform engineering teams to integrate AI capabilities into CI/CD, Infrastructure-as-Code (IaC), and DevSecOps practices.
• Evaluate emerging agent frameworks, copilots, and autonomous engineering platforms for enterprise adoption.
• Establish best practices and governance for AI-assisted engineering and operational processes.
• Define principles and reference architectures that enable AI agents to discover, access, understand, and act upon enterprise data safely and effectively.
• Partner with business and technology leaders to identify high-value opportunities for Agentic AI solutions.
• Establish enterprise standards that align data, AI, and business strategies.
• Establish standards that ensure data is: Discoverable, Well-described and semantically rich, Governed and trusted, Accessible through standardized interfaces, Consumable by both people and AI agents
• Drive adoption of metadata-driven architectures, semantic models, knowledge graphs, and business ontologies.
• Ensure enterprise data products support machine-to-machine interactions in addition to traditional analytics use cases.
• Establish standards for exposing enterprise data through APIs, semantic layers, data products, and retrieval systems.
• Partner with platform teams to develop capabilities supporting: Retrieval-Augmented Generation (RAG), Agent orchestration platforms, Tool and API discovery, Vector-based retrieval architectures, Context management and memory frameworks
• Develop patterns that allow AI agents to access enterprise knowledge securely and responsibly.
• Establish controls for data lineage, provenance, quality, explainability, and auditability.
• Partner with Security, Privacy, and Risk teams to implement responsible AI controls and secure data access practices.
• Define trust frameworks that enable AI agents to operate within approved business guardrails.
• Lead development of enterprise semantic models and shared business definitions.
• Improve metadata quality, business context, and knowledge accessibility across the organization.
• Advance enterprise knowledge management practices that enhance AI reasoning, discovery, and decision support.
• Partner with Data Platform, Engineering, Analytics, and Product teams to align technology roadmaps.
• Collaborate with BI and analytics teams to maintain consistent business metrics and semantic definitions across human and AI consumers.
• Influence technology investments that enable future AI and Agentic AI capabilities.
• Monitor emerging trends in AI, Agentic Systems, Data Architecture, and Knowledge Management.
• Evaluate innovative technologies and identify strategic adoption opportunities.
• Advise executive leadership on enterprise AI strategy and future-state architecture.

Benefits

• Base pay is one part of the Total Rewards that Comcast provides to compensate and recognize employees for their work.
• Most sales positions are eligible for a Commission under the terms of an applicable plan, while most non-sales positions are eligible for a Bonus.
• Additionally, Comcast provides best-in-class Benefits to eligible employees.
• We believe that benefits should connect you to the support you need when it matters most, and should help you care for those who matter most.
• That’s why we provide an array of options, expert guidance and always-on tools, that are personalized to meet the needs of your reality – to help support you physically, financially and emotionally through the big milestones and in your everyday life.
• Please visit the compensation and benefits summary on our careers site for more details.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior