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Provide hands-on technical support for customers using Nimble's APIs and data collection platform. Troubleshoot integrations, API and web extraction issues (captchas, bot detection, geo-restrictions), guide onboarding and implementation, investigate issues using logs and diagnostics, escalate and validate fixes with Product and Engineering, create documentation and knowledge base articles, and drive improvements to support processes and platform reliability.

Lead product marketing efforts, shaping positioning and messaging for Nimble's products. Collaborate with Sales and Product teams, and engage both business decision-makers and technical users effectively.

Manage a portfolio of enterprise customers to drive adoption, retention, renewals, and expansion. Build stakeholder relationships across data, engineering, product, and BI. Run QBRs/EBRs, monitor customer health, mitigate risks, influence product roadmap, and translate technical capabilities into measurable business value.

Lead and scale a global Customer Success team to drive net revenue retention, expansion, renewals, and adoption. Build scalable CS frameworks, partner with Sales, Product, and Finance, run executive reviews, and align customer outcomes with company goals.

Hunt and close new enterprise logos and expansions for a real-time web intelligence platform. Own full sales cycle, manage POCs, maintain Salesforce, meet quotas, and travel occasionally for customer meetings and events.

Lead and scale the revenue operations function: own CRM and RevOps stack, ensure data integrity, drive forecasting and pipeline health, build dashboards and insights, align Sales/Marketing/CS/Finance, enable territory/quota planning, and lead change to prepare the revenue engine for growth.

Design, build, and optimize Nimble's AI-native search backend: end-to-end search pipeline, performance benchmarking, API and infrastructure design, and collaboration with ML researchers to productionize LLM-powered retrieval and structured outputs.

Lead design and build of Nimble's end-to-end data infrastructure: storage, caching, indexing, orchestration, and scalability. Make architecture decisions, implement pipelines and platforms, drive cross-functional scalability, mentor engineers, and operate production data systems at large scale.

Lead technical pre-sales discovery and architect custom solutions using APIs and Python. Deliver demos, POCs, and proposals; collaborate cross-functionally; drive adoption and expansion; create reusable demo assets and run solution design workshops to de-risk implementations.

Build, own, and deploy production-grade agentic AI systems and infrastructure. Design evaluations, datasets, and metrics to improve agent quality. Ensure reliability, scalability, and product-aligned delivery for LLM-powered products.