• Lead the Privacy Engineering team’s strategy, roadmap, and execution across privacy infrastructure, data governance, and privacy-by-design initiatives • Hire, coach, and develop a team of privacy engineers while establishing clear operating rhythms, priorities, and technical standards • Guide the design and delivery of scalable privacy controls, including data discovery, classification, access controls, audit logging, retention, deletion, lineage, encryption, and key management • Partner with Legal, Compliance, Security, Product, Data, Machine Learning, and Infrastructure teams to translate privacy requirements into practical technical solutions • Oversee privacy reviews, technical risk assessments, and threat modeling for new products, data flows, models, and platform capabilities • Define metrics and communicate progress, tradeoffs, dependencies, and risks to technical and cross-functional stakeholder

• Work in collaboration with Sales, Product, Engineering, Deployment, Customer Success, LMR Customer Service Managers, and CMSO Support teams throughout the customer engagement. • Accountable and responsible for the execution of service agreements, deployment of field resources, reduction in time to resolution, and escalation management. • Exercise judgment in selection methods and techniques for obtaining solutions. • Ensures best practices are being adhered to within the customer's environment. • Delivers consistent service levels by exceeding customer expectations and managing customer escalations. • Maintains awareness of all complex service matters including technical solutions implementations and activities. • Reporting on technical performance, and providing reviews of relevant service requests open within a customer's environment. • Can explain technical problems and solutions to team/client members. • Ensures effective coordination and support between account teams and supporting technical resources.

• Lead a team of 3-4 software engineers in the consistent, timely delivery of quality features and products • Own not just timely delivery, but validating that what the team ships actually works for our customers and operators • Foster a build, test, learn culture – shipping practical MVPs and iterating on feedback rather than waiting for perfect requirements, and helping the team stay effective amid ambiguity • Cultivate fluid collaboration across the stack and minimize serialized hand-offs, so work moves without "throw it over the wall" delays • Practice and champion strong engineering methods and practices • Set the team's norms for AI-assisted development – how engineers use AI coding assistants (Claude, Copilot) to speed up prototyping, code review, debugging, and documentation - and clear the path to adoption • Help your team build good judgment about where AI tools genuinely help and where they don't, keeping the quality bar high • Adapt the team's process as AI-assisted development changes how quickly work moves, and use metrics to make those calls • Coach team members as they take on greater skills and responsibilities • Work with product managers to understand requirements, prioritize backlogs, and set timelines • Own business metrics alongside product managers and cross-functional business partners

• Own the end-to-end delivery of technical programs, many of which span hardware, software, and research efforts • Define workstreams, set milestones, and coordinate execution across multiple teams and functions • Serve as the connective tissue between engineering, research, product, and operations to ensure aligned priorities and timelines • Proactively identify and resolve bottlenecks, trade-offs, and interdependencies • Dive deep into technical topics to ensure risks are understood and addressed early • Build lightweight processes and tooling to help teams scale sustainably