Vice President of Engineering

SundaySky•New York, NY

3d•Remote

About The Position

SundaySky is seeking a Vice President of Engineering to lead the next stage of our product and platform growth. This executive will build and lead a scalable, high-performing, AI-enabled Engineering organization that delivers with speed, quality, accountability, and technical excellence. The VP of Engineering will scale the team, strengthen technical ownership, improve delivery predictability, and establish a modern software development lifecycle powered by automation, AI-assisted development, quality engineering, and measurable execution rigor. The ideal candidate has successfully built and led distributed Engineering teams, modernized development practices, implemented AI-enabled workflows, and improved delivery velocity and quality. This is a player-coach role requiring the ability to contribute directly when needed through architecture, technical design, prototyping, complex problem-solving, and code.

Requirements

10+ years of Engineering leadership experience, including leadership of managers, architects, technical leads, and distributed teams.
Experience as a VP of Engineering, Head of Engineering, Senior Director of Engineering, or equivalent leader within a SaaS, enterprise software, platform, or technology company.
Demonstrated success building, restructuring, scaling, and leading high-performing Engineering organizations.
Strong full-stack background spanning frontend, backend, APIs, data, cloud infrastructure, and integrations.
Hands-on software development experience and the ability to contribute to architecture, prototypes, technical design, code, and critical problem-solving.
Strong architectural judgment across scalable systems, cloud-native platforms, data models, security, observability, reliability, and performance.
Experience managing onshore, nearshore, and/or offshore teams and partners.
Proven success improving Engineering operating models, delivery practices, technical ownership, velocity, and predictability.
Experience implementing automation across CI/CD, testing, infrastructure, observability, security, quality gates, and release management.
Experience adopting and operationalizing AI-assisted Engineering tools and workflows.
Experience building AI-powered features, internal accelerators, workflow automation, LLM-enabled capabilities, or intelligent developer tools.
Ability to partner effectively with Product, UX, GTM, Customer Success, Finance, HR, and executive leadership.
Excellent communication skills, including the ability to explain technical strategy, risks, architecture, capacity, and tradeoffs to executive and board audiences.
Strong judgment, urgency, accountability, and the ability to lead through ambiguity, growth, and change.

Nice To Haves

Experience in B2B SaaS, enterprise software, marketing technology, fintech, financial services, insurance, or another regulated industry.
Experience with tools such as GitHub Copilot, Claude Code, Cursor, ChatGPT, or similar AI-assisted development platforms.
Experience delivering capabilities involving LLMs, agents, prompt orchestration, personalization, recommendations, content generation, workflow automation, or intelligent analytics.
Experience building organizations that use nearshore, offshore, contractor, or global delivery models.
Experience with cloud-native, API-first, data-driven, multi-tenant, configurable, or enterprise-grade platforms.
Experience improving organizations with limited roadmap visibility, backlog discipline, technical ownership, delivery accountability, or release predictability.
Experience implementing automated regression and performance testing, security scanning, observability, and production-readiness practices at scale.
Experience in a growth-stage company balancing customer commitments, platform modernization, technical debt, team scaling, and product innovation.

Responsibilities

Build, lead, and scale a high-performing Engineering organization across U.S.-based, nearshore, and offshore teams.
Define the Engineering operating model, organizational structure, ownership boundaries, technical leadership model, and delivery expectations.
Recruit, develop, and retain Engineering leaders, architects, full-stack engineers, QA professionals, DevOps resources, and technical leads.
Create a culture of accountability, urgency, collaboration, craftsmanship, technical excellence, and continuous improvement.
Establish clear performance expectations, career paths, Engineering standards, team health metrics, and leadership development plans.
Design team structures that promote focus, accountability, technical depth, and scalable product development.
Build an effective blend of employees and delivery partners to increase capacity, flexibility, and execution speed.
Establish strong onboarding, documentation, knowledge-sharing, code ownership, architecture review, and technical readiness practices.
Create repeatable processes for planning, delivery, support, technical decisions, and cross-functional coordination.
Partner with Product, Customer Success, Sales, Operations, Finance, HR, and executive leadership to align resources with company priorities.
Design and implement an AI-enabled software development lifecycle that improves productivity, quality, speed, and developer experience.
Operationalize AI-assisted tools for coding, testing, documentation, code review, refactoring, debugging, requirements interpretation, release readiness, and production support.
Integrate human technical judgment and AI acceleration across planning, architecture, development, testing, deployment, monitoring, and incident response.
Establish responsible AI development standards covering security, intellectual property, privacy, code review, test coverage, architectural consistency, and human accountability.
Partner with Product and UX to structure requirements, designs, and acceptance criteria for effective AI-assisted development.
Develop internal Engineering agents, accelerators, automation, and workflows that reduce manual work and improve throughput.
Provide technical leadership for AI-powered product capabilities, platform services, internal tools, and customer experiences.
Evaluate opportunities involving AI, automation, agents, LLM integrations, personalization, recommendations, content generation, and intelligent workflows.
Partner with Product Management to assess feasibility, architecture, cost, scalability, risk, and customer value.
Guide the architecture of AI capabilities, including model integrations, prompt orchestration, data pipelines, APIs, permissions, observability, auditability, and human-in-the-loop workflows.
Ensure AI features meet appropriate quality, security, privacy, compliance, monitoring, explainability, and governance standards.
Help move the company from AI experimentation to repeatable, production-grade capabilities.
Drive consistent, predictable, high-quality delivery across teams and initiatives.
Establish operating rhythms for planning, estimation, dependency management, technical reviews, release readiness, and executive reporting.
Implement automation-first practices across CI/CD, testing, QA, observability, security scanning, infrastructure provisioning, deployment, and release management.
Improve speed by reducing rework, strengthening requirements readiness and technical design, and creating clear ownership.
Define and track metrics such as cycle time, deployment frequency, lead time, defect escape rate, test coverage, production incidents, reliability, capacity, and release predictability.
Establish clear definitions of ready and done, automated quality gates, disciplined release practices, and measurable success criteria.
Create rapid feedback loops using customer input, production usage, support issues, incidents, and post-release data.
Translate company and product strategy into a scalable technical strategy and execution roadmap.
Lead architecture, platform scalability, reliability, security, performance, data, APIs, integrations, DevOps, AI enablement, and technical debt management.
Align Engineering investments with business outcomes, revenue priorities, customer commitments, enterprise readiness, and long-term platform health.
Establish architecture review practices that support speed, consistency, extensibility, maintainability, security, and resilience.
Balance near-term delivery priorities with long-term technical sustainability.
Create visibility into Engineering capacity, allocation, delivery status, dependencies, risks, and tradeoffs.
Manage budgets, hiring plans, contractor spend, vendors, software tooling, infrastructure costs, and resource allocation.
Identify and mitigate operational, security, scalability, architectural, delivery, personnel, compliance, and platform risks.
Establish effective incident management, production support, monitoring, alerting, reliability, and postmortem practices.
Ensure Engineering processes support security, privacy, compliance, auditability, enterprise.
Contribute directly to architecture, technical design, prototyping, troubleshooting, and implementation when needed.
Guide critical technical decisions, architecture proposals, design patterns, code quality standards, and Engineering tradeoffs.
Serve as an escalation point for complex platform, scalability, reliability, performance, integration, data, and security challenges.
Work directly with engineers to unblock delivery and model strong Engineering practices.