Solutions Engineering

Every consumer on earth purchases in one of three places: online, big-box retail, or mom-and-pop shops. Paradoxically, the largest commercial channel is, by far, humble traditional trade shops. Each analog store is digitized into a dynamic graph where noise is filtered into low latency signals, transforming the antiquated offline world into advanced digital intelligence. Commercial leaders gain the precision to see what others cannot, store by store, rendering decisive decision advantage to win the market. Relentless sense of ownership in the outcome, regardless of circumstance, acting decisively to shape the environment rather than being shaped by it. Native is hiring a Forward Deployed Solutions Engineer to sit between Solutions Engineering, customer systems, applied analytics, and deployment. It is not a support role. It is not a post-sale implementation function. The role exists to make complex enterprise customers understand exactly how Native fits into their operating environment, why it matters, what it will require, and how it will create measurable leverage. They lead discovery, demos, solution mapping, proof design, objection handling, integration scoping, and deployment planning in partnership with Account Executives. In strategic accounts, they move closer to the customer environment: data platforms, APIs, internal systems, reporting layers, workflow logic, operating constraints, and implementation risk. Native needs a technical-commercial operator who can help win the deal and then stay close enough to ensure the solution survives contact with the customer's systems, data, workflows, and field reality. Data is distributed across warehouses, BI layers, ERP systems, CRM systems, field tools, retail execution platforms, spreadsheets, internal reporting, and local operating processes. This person must understand how customer data is structured, exposed, governed, joined, analyzed, and operationalized. They must be able to reason about data quality, measurement design, model outputs, prioritization logic, API dependencies, implementation constraints, and the practical path from technical possibility to deployed value. If the deployment path is unclear, define it. If a workflow is poorly understood, document it. If the model logic is not credible, pressure-test it. If the AE needs technical force to advance the account, provide it. Performance is assessed on one axis: the ability to convert technical credibility into revenue progression, and revenue progression into durable customer value. Run discovery, demos, workshops, solution mapping, proof design, validation, objection handling, and deployment scoping. Understand how Native connects to the systems customers already use: data warehouses, BI tools, ERP, CRM, retail execution platforms, field applications, reporting layers, APIs, internal models, and workflow systems. The work must show where Native fits, what it replaces, what it strengthens, and what it needs to integrate with. Bring credibility to conversations involving measurement design, data interpretation, model outputs, prioritization frameworks, exception detection, segmentation, forecasting, and operational analytics. Help customers understand what Native can measure, what it can infer, where confidence is high, where assumptions matter, and how analytics translate into action. Identify integration requirements, API dependencies, authentication constraints, data exchange patterns, payload structures, mapping logic, governance issues, and implementation risks early in the sales process. The work must reduce ambiguity before the contract is signed, not discover preventable complexity after it. Identify where Native can strengthen existing customer workflows across commercial planning, store prioritization, retail execution, distributor management, field force productivity, issue detection, performance management, executive reporting, and market measurement. It should increase the output of the workflow. Move deeper into strategic customer environments when the account requires it. Diagnose system constraints, data realities, workflow gaps, field complexity, and implementation risk. Shape deployment strategy, support implementation design, and help convert signed contracts into measurable operating value. Create the assets required to win sophisticated buyers: technical demos, integration maps, solution architectures, workflow diagrams, proof-of-value designs, analytics examples, model logic explanations, deployment narratives, technical explainers, and customer-specific materials for executive, commercial, and technical stakeholders. Clear experience with analytics, modeling, measurement design, data interpretation, and the translation of complex data into business action. Must be able to reason about data quality, model outputs, scoring or prioritization logic, predictive frameworks, analytical tradeoffs, and operational value. Forward-Deployed Range: Able