Senior Solutions Engineer (Data & Analytics / Observability)

Meet the Team You will join the Solutions Engineering team focused on Data and Analytics, partnering closely with Sales, Product, and Marketing to help customers gain deep visibility into their applications, infrastructure, and digital experiences. Our team acts as trusted technical advisors, enabling customers to improve reliability, performance, and business outcomes across complex, hybrid and cloud-native environments. Your Impact As a Senior Solutions Engineer, you will lead the technical evaluation stage of the sales cycle and serve as a key advisor to customers and prospects. You will uncover business and technical challenges; design tailored solutions and clearly articulate the value of Splunk’s AI portfolio. This role is exciting because it sits at the intersection of observability, security, cloud, and modern DevOps/SRE practices. Responsibilities Lead discovery sessions to understand customer architectures, business goals, and observability requirements. Design and deliver compelling demos, presentations, and technical workshops across Splunk’s Observability portfolio. Own and execute proof-of-concept / proof-of-value engagements, defining success criteria and measurable outcomes. Serve as the primary technical advisor throughout the sales lifecycle, guiding customers through solution design and technical decision-making. Respond to technical components of RFPs/RFIs with clear, accurate, and customer-centric solutions. Collaborate with Sales, Marketing, and Product teams to develop impactful customer content and support industry events. Extract customer business needs and define innovative technical solution that can address the customer’s business needs Minimum Qualifications Bachelor's degree in computer science, Engineering, or equivalent education or experience. At least 3 years of experience in pre-sales, solutions engineering, Business Analytics or technical customer facing role within the observability space. Hands-on experience with Data / Log Platforms and Business Intelligence tools like Databricks, ThoughtSpot, Elastic, Tableau, Qlik Sense, Power BI, etc. Strong knowledge of cloud platforms (AWS, Azure, or GCP) and modern application architectures. Advanced knowledge of AI/ML and building custom AI models. Working knowledge of AI technologies like Python, TensorFlow, Anaconda, ONNX, PyTorch, etc. Preferred Qualifications Experience with DevOps or Site Reliability Engineering (SRE) practices. Good understanding of Datawarehouse, Data Lake, ETL, ELT, etc. Certifications like Certified Kubernetes Administrator (CKA), AWS Certified DevOps Engineer, etc. Masters/PhD in AI AI/ML certifications like TensorFlow Developer Certificate Why Cisco? At Cisco, we’re revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond. We’ve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint. Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and you’ll see that the opportunities to grow and build are limitless. We work as a team, collaborating with empathy to make really big things happen on a global scale. Because our solutions are everywhere, our impact is everywhere. We are Cisco, and our power starts with you. Cisconians power the future. We make impact as a team, innovating fast and fearlessly to create meaningful solutions on a large scale. The depth and breadth of our technology doesn't just benefit our customers – it also means limitless opportunities for us to experiment and learn. We understand the power each of our unique backgrounds bring when we work together. Because of that, we have a global network of thinkers, doers, experts, and curious creators wh ... (truncated, view full listing at source)