• Act as a hands-on contributor, designing, developing, maintaining, and scaling Coupa’s platform systems and services • Build reusable microservices, platform components, and frameworks to support standardized data flows (APIs, files, message queues) • Participate in architectural planning, code reviews, and operational design to ensure high-quality, scalable solutions • Promote engineering best practices including unit testing, automation, and clean code principles • Collaborate closely with other teams to simplify deployment, operations, and integration of platform components • Drive continuous improvement by identifying and implementing enhancements to the platform, processes, and overall business

• Analyze large-scale PBM datasets including pharmacy claims, eligibility, formulary, utilization, rebate, and pricing data. • Support executive, clinical, and operational decision-making through data-driven recommendations. • Design, develop, and optimize analytics solutions using Google BigQuery, Cloud Storage, and GCP-native tools. • Build and maintain performant SQL queries, views, and datasets for PBM performance, KPIs, and regulatory reporting. • Ensure data quality, integrity, and compliance across cloud-based data pipelines. • Develop and maintain Power Apps. • Utilize Power Automate to streamline workflows and data refresh processes. • Collaborate on Power Apps solutions to support operational and analytical use cases. • Work closely with operations, finance, clinical teams, compliance, and IT. • Partner with data engineering teams to optimize data ingestion and modeling strategies. • Translate business requirements into technical and analytical solutions. • Present complex analytical findings in clear, actionable formats to non-technical audiences. • Document data definitions, metrics, and business logic to support transparency and audit readiness.

• Supporting the Salesforce Platform Engineering team with day-to-day development and administration work • Delivering enhancements, bug fixes, and configuration aligned to team standards • Handle incoming Salesforce support requests: investigate issues, reproduce bugs, perform root-cause analysis, and implement fixes or escalations as needed. • Partner with senior engineers and admins to triage and resolve incidents impacting Sales, Marketing, Customer Success, and other GTM teams. • Monitor platform health (e.g., error logs, integration failures, performance concerns) and surface risks or recurring issues to the team. • Implement declarative configuration in Salesforce: objects, fields, page layouts, record types, Flows, validation rules, assignment rules, and approval processes. • Develop and maintain custom solutions using Apex, Lightning Web Components (LWC), and APIs, following coding standards and patterns set by the Salesforce Platform Engineering team. • Support feature delivery across Sales Cloud and related clouds (e.g., Service Cloud, Experience Cloud, CPQ) under the direction of senior engineers. • Support integration troubleshooting between Salesforce and other enterprise systems (e.g., ERP, product, customer success platforms) by reviewing logs, payloads, and error messages, escalating complex issues as needed. • Contribute to data quality initiatives, including deduplication, data updates, and enforcing validation and governance standards. • Collaborate with business stakeholders, admins, engineers, and analysts to understand requirements and clarify acceptance criteria. • Maintain documentation for configuration, customizations, integrations, and runbooks to support operational excellence. • Other duties as assigned.

• Architect the Engine: Design and build a production-grade Agent Runtime that handles tool registration, permissions, timeouts, and retry semantics for autonomous agents. • Define the Standard: Evolve our internal GenAI Client and SDKs to create a "paved road" for product engineers, standardizing how every team at Light interacts with LLMs. • Solve for Trust: Build the "CI/CD for AI", implementing reliability frameworks, golden datasets, and regression testing pipelines that allow us to deploy prompts and agents with confidence. • Expand the Ecosystem: Lead the integration of Light with external platforms via the Model Context Protocol (MCP) and automation tools like n8n. • Governance & Scale: Design the guardrails, rate limits, and caching strategies that ensure our AI infrastructure scales predictably and cost-effectively.