EnrollHere, Inc.

Engineering Manager

• Own end‑to‑end engineering delivery across the full product surface — enrollment workflows, the commission engine, AI‑driven compliance scoring, telephony infrastructure, and the underlying platform that ties it all together
• Drive sprint execution, release cadence, and delivery predictability without losing the pace a PE‑backed growth company possess
• Establish engineering processes that scale without the bureaucracy that kills momentum — lightweight rituals, clear ownership, and fast decision‑making
• Transform the Product Development Lifecycle to incorporate agents and autonomous capabilities driving increased innovation
• Build a culture where engineers feel real ownership of outcomes, not just tickets. That means clarity of priorities, transparency around tradeoffs, and a bias (not hard line) toward finishing work, not starting more of it
• Set expectations for how the team collaborates: thoughtful code reviews, healthy debate, and a shared commitment to raising the bar on quality and reliability
• Create an environment where engineering, QA, and product operate as one delivery unit — aligned on goals, honest about risks, and accountable to the same definition of “done.”
• Anchor decisions in customer impact. Engineers should understand how their work shows up in an agent’s workflow, a carrier’s integration, or a beneficiary’s enrollment experience — and use that context to guide tradeoffs, simplify complexity, and eliminate friction
• Model the behaviors you expect, such as: direct communication, curiosity, urgency without panic, and a willingness to roll up your sleeves when the team needs it
• Own technical direction and architectural strategy across the platform
• Make pragmatic architectural decisions — right-sized to where the company is today, with a clear-eyed view of where it needs to go
• Manage technical debt actively and transparently — it does not need to be zero, it needs to be known, tracked, and directionally improving
• Identify and implement AI and automation capabilities that materially improve engineering velocity, code quality, and security — not as a pilot program but as a core operating practice
• Evaluate and leverage technology partnerships that accelerate capability without creating dangerous dependencies
• Build a team that treats AI tooling as a standard part of the engineering workflow
• Partner across teams to build an AI innovation roadmap that puts customers first, woven into the product naturally — not bolted on
• Lead and develop a team of engineers across varying levels of seniority, experience, and working style that does not compromise the culture, but promotes it
• Build a collaborative, entrepreneurial engineering culture — one where ownership is shared, standards are high, and people do their best work without burning out
• Hire, develop, and when necessary, make hard calls on team composition or individual performance accountability
• Create clarity of role and accountability for senior engineers, including those operating in informal leadership positions
• Own the organization's security posture as an AI-augmented engineering practice, embedding automated code analysis, threat detection and policy enforcement directly into the development and deployment lifecycle
• Establish a quality culture reinforced by AI and automation, using intelligent test generation, continuous regression detection, and clear quality gates so reliability and coverage are enforced by default, not inspection
• Improve platform resilience through AI‑driven observability and disciplined releases, leveraging anomaly detection and automated root‑cause analysis to reduce incidents and eliminate repeat failures
• Ensure regulatory and contractual compliance is engineered into the platform, using automated controls, continuous validation, and auditable enforcement for HIPAA‑adjacent data handling and carrier obligations
• Translate compliance requirements into executable, AI‑testable engineering standards in partnership with compliance and operations, ensuring consistency and repeatability across teams and releases
• Champion a data‑ and AI‑powered feedback loop across engineering, QA, and production, enabling earlier defect detection, automatic regression prevention, and continuous system‑level learning

Head of Engineering