Senior Search Data and Systems Engineer, Organic and Agentic Search

Autodesk is a company that helps innovators turn their ideas into reality through software. They are seeking a Senior Search Systems Engineer to build intelligence and automation layers for marketing and AI visibility data, transforming structured data into decision frameworks and automated insights.

Responsibilities:

• Define the data, instrumentation, and custom dimensions required to support SEO, AI visibility, content performance, and entity-level analysis across Autodesk’s marketing ecosystem
• Partner with SQL developers, analytics engineers, and BI teams to operationalize scalable datasets, transformation logic, reporting tables, and analytical data models that support downstream reporting, automation, and decision systems
• Shape ingestion and normalization strategies for search, behavioral, content, and AI visibility datasets across APIs, warehouses, and marketing platforms
• Ensure data quality, consistency, and governance across SEO and AI visibility reporting systems by validating metric definitions, transformation logic, and analytical outputs
• Translate ambiguous business questions and SEO hypotheses into structured technical requirements that can be implemented across reporting pipelines, analytical systems, and automation workflows
• Define analytical frameworks and scoring logic for evaluating brand visibility, entity coverage, content performance, and competitive presence across traditional and AI-driven search environments
• Develop monitoring and alerting systems that identify meaningful shifts in search visibility, AI model behavior, response patterns, or competitive movement over time
• Build and maintain automation, prioritization models, and agent-like systems that transform curated datasets into actionable recommendations for SEO, content, and discovery optimization
• Translate marketing strategies and initiatives into scalable technical implementations, including rule-based systems, experimentation frameworks, and applied AI workflows
• Prototype and evaluate emerging tools, APIs, and frameworks related to LLM analysis, AI agents, search intelligence, and marketing automation
• Partner with search, content, analytics, engineering, and platform teams to ensure search intelligence systems and AI-driven insights are embedded into planning, prioritization, experimentation, and execution workflows
• Document system logic, assumptions, frameworks, and operational processes to ensure long-term scalability, maintainability, and organizational clarity

Requirements:

• 7+ years of experience in software engineering, applied analytics, search systems, or technical marketing roles that required designing and owning complex systems end-to-end
• Experience partnering with data engineering or analytics engineering teams to define transformation logic, data models, instrumentation requirements, and reporting outputs
• Strong understanding of marketing and analytics data architecture, including event-level data, custom dimensions, warehouse modeling, and reporting layer design
• Experience working with APIs, structured datasets, and large-scale analytical environments such as Snowflake, BigQuery, or similar cloud data platforms
• Strong proficiency in Python, SQL, or similar languages, with an emphasis on building durable systems, not one-off analyses or prototypes
• Experience designing analytical or decision systems that sit downstream of a data warehouse, including defining business logic, evaluation frameworks, and failure modes
• Deep familiarity with search, discovery, or ranking systems (traditional or AI-driven), and the ability to reason about probabilistic outputs, model variance, and imperfect signals
• Hands-on experience evaluating, prototyping, or productionizing AI-driven workflows, LLM-based systems, or agent-like architectures, with a pragmatic approach to risk and complexity
• Ability to bridge technical implementation and business strategy by translating analytical needs into scalable data and systems requirements
• Demonstrated experience operating in ambiguous problem spaces, where requirements were incomplete, tooling was immature, and success depended on judgment rather than predefined best practices
• Proven ability to influence cross-functional partners by explaining technical tradeoffs clearly and pushing back when solutions are premature, fragile, or misaligned
• Track record of documenting systems, assumptions, and decisions to support long-term maintainability and team learning
• Comfort being the first or only person in a role, and helping define what 'good' looks like before metrics or playbooks exist