Job Opportunities API

About MediaRadar MediaRadar , equips marketing, sales and analytics leaders with the intelligence they need to stay ahead. Our platform delivers always-on, AI-enabled Creative, Competitive, Commercial and Market Intelligence—spanning ad strategy, media spend, creative assets and brand messaging across 30+ media channels and five million brands. With deep insights into more than 35 million ad and campaign assets and $280 billion in media spend, MediaRadar provides a single, interoperable source of truth that plugs seamlessly into enterprise analytics and AI systems. The result: faster, cleaner and more actionable intelligence that drives competitive advantage.  Job Summary We are seeking a Citizen Automation / Prompt Engineer to work closely with Data Operations and Governance to identify, develop, and deploy AI and automation solutions that drive continual scale, efficiency, and effectiveness in data delivery and data quality. The focus is on incremental, self-contained, plug-and-play solutions that improve a workflow — not on building large models or standing up heavy systems. This role is the builder behind “AI does the work, people validate it,” turning manual, repetitive, and bottlenecked tasks into reliable AI-assisted and automated workflows. This is a hybrid role for a domain expert who is fluent with AI tools but not necessarily a traditional software engineer. You will partner with Data Ops to find high-value opportunities, with Governance to keep solutions aligned to rules and definitions, and with validators to confirm output quality. When a need outgrows what a small, contained solution can deliver, this role becomes the conduit to larger-scale deployment — helping define clear requirements that engineering and platform teams can build against. Requirements Responsibilities Partner with Data Operations and Governance to identify high-value opportunities for AI and automation across data delivery and data quality workflows. Build small, self-contained, plug-and-play solutions — prompts, low-code workflows, scripts, and lightweight agents — that deliver incremental efficiency without standing up large models or heavy systems. Translate governance rules, definitions, and taxonomies into prompt and automation logic that produces accurate, consistent output. Prototype rapidly, measure operational impact, and iterate solutions based on real results and validator feedback. Deploy solutions into production data operations and support adoption, monitoring, and handoff. Recognize when a need exceeds what a contained solution can deliver, and serve as the conduit to larger-scale deployment by helping define clear, actionable requirements for engineering and platform teams. Maintain prompt libraries, automation assets, and documentation with clear versioning. Track solution performance against scale, efficiency, accuracy, and consistency targets and report on the gains realized. Stay current on AI and automation techniques and responsible-AI practices, and share them across teams. Success in this role will be measured by: Scale – Increased volume of data delivery and quality work handled without proportional growth in manual effort or headcount. Efficiency – Measurable reduction in manual effort and cycle time across targeted workflows. Effectiveness – Improved accuracy and consistency of AI-assisted and automated output. Right-Sized Delivery – Incremental, self-contained solutions delivered quickly; needs that exceed that scope are escalated with clear requirements rather than over-built. Adoption – Solutions deployed and actively used in production Data Ops workflows, not left as prototypes. Opportunity Pipeline – A healthy, prioritized pipeline of identified and delivered automation opportunities. Alignment – Solutions reflect governance rules and standards, with human validation operating as designed. Key Qualifications and Role Requirements