Deputy Manager, Analytics & Insights

• 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)

• 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.

• 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 Data Governance frameworks for sensitive financial or customer information

• Hands-on experience with Snowflake Container Services (SPCS) for running data-intensive applications
• Deep expertise in building and deploying interactive data applications using Streamlit, or professional-grade full-stack frameworks such as FastAPI for backend service architecture and ReactJS for high-fidelity financial frontends
• Advanced knowledge of Cloud Infrastructure (AWS/Azure) for managing the deployment lifecycle of machine learning models and high-scale finance applications (e.g., CI/CD, API Gateways, and monitoring)

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