Market Intelligence

NielsenIQ•Chicago, IL

About The Position

NIQ is seeking a Sr. Director, Engineering to lead AI-first transformation across customer-facing SaaS product engineering capabilities. This leader will shape technical direction, drive engineering excellence, and deliver scalable, reliable, and high-impact product outcomes in a highly matrixed, globally distributed environment. This is a people leadership role that requires deep software engineering roots. The successful candidate will be credible with senior engineers and architects, comfortable engaging in critical system design and architecture decisions, and able to translate complex technical direction into clear product, customer, and business outcomes. The role is accountable for engineering delivery outcomes and technical direction while operating through senior engineering leaders, technical leaders, architects, and cross-functional partners. The ideal candidate is a constructive challenger who can question legacy assumptions, elevate engineering standards, and guide mature SaaS capabilities toward an AI-first future.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related technical field.
15+ years of progressive technology and software engineering leadership experience.
Proven experience leading and developing senior engineering leaders and technical leaders, including Directors, Senior Managers, Principal Engineers, or equivalent roles.
Demonstrated success leading engineering for customer-facing enterprise SaaS products, data-intensive platforms, analytics products, or comparable large-scale commercial software platforms.
Experience driving engineering transformation across mature SaaS environments, including modernization, technical debt reduction, quality improvement, and delivery acceleration.
Current experience leading AI-enabled product capability development within enterprise SaaS or comparable commercial software environments.
Experience applying GenAI tools and AI-assisted practices within engineering workflows to improve software delivery, quality, developer productivity, or modernization outcomes.
Experience leading globally distributed engineering teams in a matrixed organization.
Deep software engineering background with strong system design, distributed systems, and architecture experience.
Strong understanding of cloud-native architecture, scalable platform design, data-intensive systems, API design, integration patterns, performance optimization, reliability engineering, and enterprise architecture principles.
Prior hands-on engineering experience and leadership familiarity with technologies used by modern SaaS teams, including Java, Python, Angular, Azure cloud services, and AI/GenAI technologies and frameworks.
Ability to evaluate architectural options, challenge design assumptions, and guide technical tradeoffs across speed, quality, scalability, cost, security, maintainability, and customer impact.
Strong understanding of modern software development practices, including Agile delivery, CI/CD, automated testing, observability, secure engineering practices, and production operations.
Transformative mindset with the ability to move teams from established SaaS operating models toward AI-first product and engineering approaches.
Ability to challenge the status quo constructively, challenge self and teams to improve, and bring others along through influence, clarity, and technical credibility.
Strong executive presence, stakeholder management, and communication skills across technical and non-technical audiences.
High accountability for engineering outcomes, customer impact, and business-aligned execution.
Ability to operate effectively in ambiguity, make decisions with incomplete information, and create alignment across competing priorities.
Strong coaching and talent development skills, with a track record of building high-performing engineering leadership teams.

Nice To Haves

Experience leading AI-driven product transformation initiatives that created measurable customer value or commercial impact.
Experience with modern AI/GenAI application patterns, orchestration frameworks, model integration, retrieval-augmented generation, agentic workflows, or AI-enabled analytics experiences.
Experience in retail, CPG, consumer measurement, market intelligence, data analytics, or adjacent data-rich enterprise domains.
Background working in product-led technology organizations with close partnership across Product Management, Design, Architecture, Data Science, Security, and Business stakeholders.
Experience improving engineering operating models through metrics, platform thinking, engineering productivity practices, and production excellence.

Responsibilities

Lead the evolution of customer-facing SaaS product engineering capabilities from a mature SaaS model toward an AI-first product and engineering model.
Drive AI-enabled product innovation that improves customer workflows, decision support, automation, insight generation, and product differentiation.
Apply practical understanding of GenAI tools and AI-assisted engineering practices to improve SDLC effectiveness, developer productivity, software quality, testing, documentation, and modernization efforts.
Partner with product, architecture, security, privacy, data governance, and responsible AI stakeholders so AI-enabled capabilities are implemented thoughtfully within enterprise standards.
Define and communicate technical direction for customer-facing SaaS product engineering capabilities in alignment with product strategy, business priorities, and long-term platform evolution.
Provide deep technical leadership in architecture reviews, system design discussions, and critical engineering tradeoff decisions.
Guide the design and modernization of large-scale distributed systems, cloud-native services, APIs, data-intensive product platforms, and integrated user experiences.
Challenge existing technical approaches and identify opportunities to improve scalability, reliability, performance, security, maintainability, cost efficiency, and customer value.
Raise the engineering bar around system design, design discipline, architectural decision-making, and long-term technical sustainability.
Drive engineering delivery outcomes across complex, customer-facing SaaS product capabilities while ensuring alignment across Product, Architecture, Engineering, and Business stakeholders.
Translate product and business goals into executable engineering direction, prioritization, and delivery plans through senior engineering leaders and technical leaders.
Balance new AI-first capability development with modernization of mature enterprise SaaS systems, technical debt reduction, and continuity of existing customer commitments.
Improve measurable engineering outcomes, including delivery predictability, release quality, engineering productivity, modernization progress, and customer-impacting delivery velocity.
Contribute to planning, prioritization, resource allocation, and engineering investment tradeoff decisions in partnership with cross-functional stakeholders.
Ensure customer-facing SaaS capabilities are designed, delivered, and operated with strong standards for reliability, availability, scalability, performance, security, and quality.
Strengthen engineering practices across CI/CD, test automation, observability, operational readiness, incident learning, and continuous improvement.
Promote a production-first mindset that treats operability, supportability, resiliency, and cost efficiency as core design considerations.
Use engineering metrics and operational signals to identify systemic issues, improve team effectiveness, and guide technical and organizational improvements.
Lead through direct leadership accountability, senior technical credibility, and matrixed influence across Engineering, Product, Architecture, Business, and enabling functions.
Develop and mentor Directors, Senior Managers, Principal Engineers, and senior technical talent, building leadership capacity and raising the technical bar across teams.
Create a culture of ownership, accountability, constructive challenge, customer focus, innovation, and continuous learning.
Align globally distributed teams across time zones, cultures, and organizational boundaries.
Communicate clearly with executive, technical, and non-technical audiences, translating complex engineering topics into business-relevant decisions and outcomes.