Manager, Data Analytics

LiveRamp is the data collaboration platform of choice for the world’s most innovative companies. A groundbreaking leader in consumer privacy, data ethics, and foundational identity, LiveRamp is setting the new standard for building a connected customer view with unmatched clarity and context while protecting precious brand and consumer trust. LiveRamp offers complete flexibility to collaborate wherever data lives to support the widest range of data collaboration use cases—within organizations, between brands, and across its premier global network of top-quality partners. Hundreds of global innovators, from iconic consumer brands and tech giants to banks, retailers, and healthcare leaders turn to LiveRamp to build enduring brand and business value by deepening customer engagement and loyalty, activating new partnerships, and maximizing the value of their first-party data while staying on the forefront of rapidly evolving compliance and privacy requirements. 
 The Manager, Data Analytics will lead a team of 4–6 analysts delivering reporting and analytics for LiveRamp customers, powered by LiveRamp's Clean Room. This is a player-coach role: you will own the growth, quality, and throughput of the team while staying hands-on with customer data yourself on our most complex and highest-visibility engagements. You will partner closely with Customer Success, Product, and Engineering, and act as the senior analytics voice in front of customer stakeholders. You Will: Manage, coach, and develop a team of data analysts and senior data analysts—setting goals, giving regular feedback, running performance and career development conversations, and hiring to grow the team. Own delivery across the team's portfolio: allocate analysts across customer engagements, set priorities and timelines, and hold the bar on quality and client-readiness of every deliverable. Stay hands-on. Personally lead analyses on complex, ambiguous, or strategically important engagements, and serve as the reviewer of record for methodology and interpretation on the rest. Build for scale. Invest the team's time in analytics systems, templates, and reusable libraries of analysis that generalize across customer use cases and verticals rather than one-off work. Establish and enforce standards for analytical rigor, peer review, documentation, and reproducibility—including for observational analytics on multi-dimensional datasets and experimental designs. Serve as a senior point of contact for customer stakeholders: shape requirements, present findings, handle escalations, and build durable relationships with analytics and marketing leaders. Translate what the team learns from customers into product direction, working with Product and Engineering on the measurement roadmap and on new offerings in the analytical space. Drive adoption of AI across the team's workflows—developing new applications and tools for measurement data analysis to expand product capability and improve…