Senior Director, Engineering | Retail Analytics / CPG / Market Intelligence at NielsenIQ

Chicago, Illinois, United States -

Full Time

Start Date

Immediate

Expiry Date

04 Oct, 26

Salary

200000.0

Posted On

06 Jul, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Transformation, SaaS Engineering, System Design, Distributed Systems, Cloud-Native Architecture, GenAI, People Leadership, Technical Strategy, API Design, CI/CD, Agile Delivery, Stakeholder Management, Java, Python, Angular, Azure

Industry

Software Development

Description

Company Description NIQ is the world’s leading consumer intelligence company, delivering the most complete understanding of consumer buying behavior and revealing new pathways to growth. In 2023, NIQ combined with GfK, bringing together the two industry leaders with unparalleled global reach. With a holistic retail read and the most comprehensive consumer insights—delivered with advanced analytics through state-of-the-art platforms—NIQ delivers the Full View™. Job Description About the Role We are 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. Key Responsibilities AI-First Product and Engineering Transformation 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. Engineering Strategy, Architecture, and System Design 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. SaaS Product Engineering Delivery 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. Production Excellence and Engineering Operations 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. Leadership, Talent, and Matrixed Influence 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. Qualifications Experience 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. Technical Expertise 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. Leadership Capabilities 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. Preferred Qualifications 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. What Success Looks Like AI-enabled product capabilities are being delivered with clear customer value, measurable product impact, and strong engineering foundations. GenAI-enabled engineering practices are adopted in practical, measurable ways that improve productivity, quality, modernization, and developer effectiveness. Mature SaaS capabilities are modernized without compromising reliability, performance, security, customer commitments, or delivery predictability. Architecture and system design decisions are clearer, better documented, and aligned with long-term platform and product direction. Engineering leaders and senior technical talent are operating at a higher bar, with stronger ownership, accountability, and technical judgment. Product, Architecture, Engineering, Business, and enabling stakeholders are aligned around priorities, investment tradeoffs, and delivery outcomes. Customer-facing systems demonstrate measurable improvements in reliability, scalability, performance, quality, and speed of innovation. Ideal Candidate Profile The ideal candidate is a deeply technical engineering leader who has grown into a strategic product engineering executive. They have strong software engineering roots, deep system design judgment, and the credibility to engage senior engineers and architects on complex technical tradeoffs. They have led enterprise SaaS product transformation, with particular strength in applying AI and GenAI to customer-facing product capabilities. They also understand how AI can improve engineering practices, but they treat AI adoption as a means to improve customer value, quality, speed, and differentiation rather than as a technology trend by itself. They are a constructive challenger: intellectually curious, self-critical, comfortable questioning legacy approaches, and skilled at bringing people along in a highly matrixed environment. They combine technical depth, people leadership, product orientation, and execution discipline to create durable impact across distributed teams. Additional Information Our Benefits Flexible working environment Volunteer time off LinkedIn Learning Employee-Assistance-Program (EAP) NIQ may utilize artificial intelligence (AI) tools at various stages of the recruitment process, including résumé screening, candidate assessments, interview scheduling, job matching, communication support, and certain administrative tasks that help streamline workflows. These tools are intended to improve efficiency and support fair and consistent evaluation based on job-related criteria. All use of AI is governed by NIQ’s principles of fairness, transparency, human oversight, and inclusion. Final hiring decisions are made exclusively by humans. NIQ regularly reviews its AI tools to help mitigate bias and ensure compliance with applicable laws and regulations. If you have questions, require accommodations, or wish to request human review were permitted by law, please contact your local HR representative. For more information, please visit NIQ’s AI Safety Policies and Guiding Principles: https://nielseniq.com/global/en/info/niqs-ai-safety-policies/ About NIQ NIQ is the world’s leading consumer intelligence company, delivering the most complete understanding of consumer buying behavior and revealing new pathways to growth. In 2023, NIQ combined with GfK, bringing together the two industry leaders with unparalleled global reach. With a holistic retail read and the most comprehensive consumer insights—delivered with advanced analytics through state-of-the-art platforms—NIQ delivers the Full View™. NIQ is an Advent International portfolio company with operations in 100+ markets, covering more than 90% of the world’s population. For more information, visit NIQ.com Want to keep up with our latest updates? Follow us on: LinkedIn | Instagram | Twitter | Facebook Our commitment to Diversity, Equity, and Inclusion At NIQ, we are steadfast in our commitment to fostering an inclusive workplace that mirrors the rich diversity of the communities and markets we serve. We believe that embracing a wide range of perspectives drives innovation and excellence. All employment decisions at NIQ are made without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, marital status, veteran status, or any other characteristic protected by applicable laws. We invite individuals who share our dedication to inclusivity and equity to join us in making a meaningful impact. To learn more about our ongoing efforts in diversity and inclusion, please visit the https://nielseniq.com/global/en/news-center/diversity-inclusion Career Site Team: Technology Compensation: USD 151800 - USD 200000 - yearly

Responsibilities

Lead the AI-first transformation of customer-facing SaaS product engineering and define the technical direction for scalable, data-intensive platforms. Manage globally distributed engineering teams while balancing new AI innovation with the modernization of legacy enterprise systems.