Deputy Manager, Analytics & Insights

About Zscaler Zscaler accelerates digital transformation to ensure our customers can be more agile, efficient, resilient, and secure. As an AI-forward enterprise, we are constantly pushing the envelope, leveraging the world’s largest security data lake to power our cloud-native Zero Trust Exchange platform. This innovation protects our customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. Here, impact in your role matters more than title and trust is built on results. We say, impact over activity. We seek innovators who actively use AI to amplify their impact and who thrive in an environment where we leverage intelligent systems to stay ahead of evolving threats. We believe in transparency and value constructive, honest debate—we’re focused on getting to the best ideas, faster. We build high-performing teams that can make an impact quickly and with high quality. To do this, we are building a culture of execution centered on customer obsession, collaboration, ownership, and accountability. We value high-impact, high-accountability with a sense of urgency where you’re enabled to do your best work and embrace your potential. If you’re driven by purpose, thrive on solving complex challenges, and want to be part of the team that’s helping to secure the AI age, we invite you to bring your talents to Zscaler and help shape the future of cybersecurity. Role We are looking for a Deputy Manager - Analytics and Insights, FP&A to join our team. This is a Hybrid (Specify office presence requirements: Mohali Office) role, reporting to the Manager - Analytics and Insights, FP&A in the FP&A department. As Deputy Manager, you will be the technical architect behind Finance’s next-generation AI/ML data infrastructure. Moving beyond traditional data engineering, you will bridge the gap between financial strategy and production-grade ML systems. You will lead the development of our proprietary Finance Governance Layer, manage centralized Feature Stores for predictive churn and revenue signals, and pioneer Skill Engineering to build autonomous financial capabilities. Leveraging Snowflake Container Services (SPCS), Docker, and Streamlit, you will transform the Finance data stack into a high-performance, AI-driven engine. What you’ll do (Role Expectations) - Design and develop modular "Financial Skills"—programmable AI functions (e.g., automated variance detection, anomaly engines) that perform discrete, high-impact financial tasks - Build and govern centralized Feature Stores in Snowflake to standardize predictive inputs such as churn propensity scores, renewal health, and expansion opportunity signals - Architect and own the technical "Governance Layer" for Finance, ensuring AI/ML models interact securely with sensitive data via strict RBAC and auditable frameworks - Architect production-grade data models in DBT and build interactive executive tools in Streamlit, ensuring a seamless flow from raw data to actionable, high-fidelity insights - Architect the end-to-end deployment lifecycle for financial microservices and apps using Docker, managing deployments across Snowflake Container Services (SPCS) while ensuring compatibility and portability within enterprise cloud environments like AWS (ECS/EKS) or Azure (AKS) Who You Are (Success Profile) - Act as an owner with a strong passion for the mission and a natural bias for action, operating with integrity and navigating seamlessly between high-level strategy and hands-on execution. - Serve as a high-trust collaborator who is ambitious for the team, actively giving and receiving ongoing feedback with candor, clarity, and respect to cultivate a high-performance culture. - Build as a pragmatic creator who is obsessed with iterating and shipping, showing no hesitation to roll up your sleeves to balance technical excellence with rapid user value. - Stay completely data-driven by leveraging analytics to find the truth, measure what truly matters, and replace assumptions with evidence to achieve superior outcomes. - Think at scale by connecting day-to-day execution to the broader global mission, creating robust solutions and processes designed to support a high-growth organization. What We’re Looking for (Minimum Qualifications) - Demonstrated curiosity and active exploration of AI tools, with a proven history of integrating new technologies to enhance daily workflows and augment problem-solving - 5–8 years in Data Engineering, ML Engineering, or a highly technical Finance Analytics role - Expert SQL and Python proficiency (specifically for data engineering, automation, and deploying ML-ready pipelines) - Proven experience in deploying containerized applications and services within modern cloud ecosystems (e.g., AWS, Azure, or Snowflake SPCS) - Significant experience with DBT (standards, testing, CI/CD) and Snowflake (performance tuning, architecture) - Experience building or managing D