Marketing Al Systems & Process Manager

Okta•San Francisco, WA

20d•$125,000 - $210,000•Hybrid

About The Position

At Okta, the AI strategy aims to revolutionize customer engagement and business operations. For this strategy to succeed, a deep, practical understanding of current Go-to-Market (GTM) motions is essential. The Marketing AI Systems & Process Manager is a strategic and critical role designed to connect Technology Data & Insight, Marketing Operations, and the Marketing business unit to build and scale AI Marketing initiatives. You will be embedded within the Marketing Strategy & Operations team, becoming the foremost expert on their end-to-end workflows. Your mission will be to map the current state, identify friction points, and pinpoint the highest-impact opportunities for AI implementation . This role bridges business needs with technical solutions by selecting appropriate AI technologies, defining data pipelines, developing and integrating AI models and APIs, and collaborating with data scientists and marketing teams to drive data-backed strategies and enhance customer experiences. Working closely with enterprise and solution architects, you will focus on building a robust Marketing systems infrastructure for AI . This position is ideal for a process-obsessed operator who excels at connecting business challenges with technology-driven solutions. You will act as a change agent, helping the organization adopt an AI-driven mindset.

Requirements

5+ years of experience in a Marketing Operations, Marketing Solutions Architect, Marketing Data Operations, Marketing Systems Engineer, or related role.
Demonstrated expertise in business process mapping, analysis, and re-engineering.
Proficient with tools like Visio, Lucidchart, or similar process documentation software; you naturally think in flowcharts and are driven to find more efficient ways of working.
Proven experience managing complex, cross-functional projects from discovery through to implementation.
Strong analytical skills and a highly structured, detail-oriented approach to problem-solving.
Strong understanding of machine learning, data pipelines, cloud infrastructure, APIs, and various AI/ML frameworks.
Knowledge of DevOps workflows and tools, such as Git, containers, Kubernetes, and CI/CD.
Direct experience implementing or administering a major GTM system (e.g., Salesforce, Marketo, 6sense, Tableau, etc).
Strong understanding of data management principles, including data governance, data quality, data security, and data lifecycle management.
Knowledge of data integration and interoperability between systems (e.g., APIs, middleware, data lakes/warehouses).
Ability to design and build complex, scalable, and secure systems.
In-depth knowledge of components and architectural trade-offs involved across data management, governance, model building, deployment, and production workflows of AI.
Exceptional communication and interpersonal skills, with the ability to build trust and influence stakeholders at all levels.
Excellent ability to communicate technical concepts and strategies to both technical and non-technical stakeholders.
A collaborative mindset is essential to harmonize relationships among various stakeholders.

Nice To Haves

Experience with process improvement methodologies like Lean or Six Sigma.
Familiarity with the current landscape of GTM AI tools (e.g., conversational AI, lead scoring, forecasting, content generation).
Experience working in a change management capacity, helping teams adopt new technologies and processes.
Knowledge of marketing platforms (e.g., marketing automation, CMS, DAM), customer journey mapping, and marketing operations.
Proficiency with SQL and business intelligence (BI) tools and reporting platforms (e.g., Tableau, Looker).
Knowledge of advanced analytics tools (e.g., SAS, R, Python) and ML/Deep Learning frameworks (e.g., TensorFlow).

Responsibilities

AI Operations & Strategy: Partner with the Marketing AI team to design the AI strategy for marketing operations, ensuring AI solutions meet specific business needs and align with overall objectives.
Partner with Marketing Technology to define and own the multi-year roadmap for AI projects and integrations.
Partner with Business Technology, Data & Intelligence (TDI) to design and build scalable, secure, and cost-effective AI systems, managing potential risks.
Play a key role in defining the AI architecture and selecting appropriate technologies, ensuring new tools are well-integrated with existing data management and analytics tools.
Process Mapping & Optimization: Conduct deep-dive discovery with teams across the GTM organization, documenting and visualizing critical Marketing business processes end-to-end (e.g., Lead-to-Opportunity, Inbound Lead Flows), creating a definitive library of "as-is" workflows
Analyze existing processes and partner with the Marketing AI team to identify bottlenecks, manual tasks, and pain points ripe for AI-driven automation and optimization, quantifying the potential impact of these improvements.
AI Use Case Translation & Implementation: Serve as the primary liaison, translating business needs and process gaps into clear requirements for potential AI solutions and vendors.
Oversee the design and implementation of AI models and their conversion into Application Programming Interfaces (APIs) for widespread use in marketing campaigns.
Collaborate within the Marketing Data Operations and Technology team and other AI professionals to augment transformation efforts by identifying and piloting use cases.
Discuss the feasibility of these use cases and architectural design with business teams, translating business leaders' visions into realistic technical implementation, while also highlighting misaligned or impractical initiatives.
Build and own AI workflows and automation in existing Marketing technology, such as Marketo, 6sense, Clay, Zapier, etc
Partner with the Business Technology, Data & Intelligence team to plan and execute the implementation of new GTM AI tools/automations, managing project timelines, dependencies, and user acceptance testing for designated areas.
Collaboration & Stakeholder Management: Collaborate effectively with data scientists, data engineers, developers, operations, and business unit leaders to govern and scale AI initiatives and drive tangible business value.
Align technical implementation with existing and future requirements by gathering inputs from multiple stakeholders and developing processes and products based on these inputs.
Work closely with leaders in Marketing Strategy & Operations, GTM Operations, and Marketing to understand priorities, gain buy-in for process changes, and ensure new solutions are adopted effectively.
Provide strategic technology landscape assessment to drive capability development and champion success by identifying contributions toward strategic initiatives.
Tool Ownership & Governance: Own the administration and strategy for specific GTM AI applications.
Develop best practices, create documentation, and provide support to maximize investment value.
Govern best practices for data integrity, compliance (e.g., GDPR, CCPA, SOC2), and system security within the AI ecosystem.
Data Architecture & Pipelines: Collaborate within the Marketing Data Operations and Technology team on requirements for data flows, data lakes, and semantic mappings to ensure high-quality data inputs for AI models.
Enable business stakeholders through integration and architecture documentation, data governance models, and training on system best practices.
Risk Management & Ethical AI: Work closely with security and risk leaders to foresee and overturn risks, ensuring ethical AI implementation and trust in AI systems.
Stay informed about upcoming regulations and map them to best practices.
Understand data privacy, potential for manipulation, and the importance of ethical considerations in AI applications.
Innovation & Continuous Improvement: Stay informed about emerging AI trends, autonomous agents, and multi-agent systems to keep marketing initiatives at the cutting edge.
Audit AI tools and practices across data, models, and software engineering with a focus on continuous improvement, ensuring a feedback mechanism to assess AI services, support model recalibration, and retrain models.