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Leads Duck Creek Technologies’ Intelligence function, overseeing analytics engineering, business intelligence, reporting, dashboards, metrics, and insight generation. Builds scalable analytics platforms and governance standards, partners with product, AI, engineering, and business leaders, and translates complex data into actionable recommendations. Manages analytics teams and advances enterprise data-driven decision-making, operational performance, and product development.

Leads financial planning and analysis for a SaaS business, including bookings and revenue forecasting, long-range planning, scenario modeling, annual planning, KPI reporting, and board materials. Partners with GTM, Sales Operations, FP&A, and senior leadership to analyze SaaS metrics, improve forecast accuracy, support quota and commission design, and translate financial insights into strategic recommendations.

Leads sales operations strategy and execution, including forecasting, planning, budgeting, quota allocation, compensation administration, CRM optimization, pipeline analysis, KPI reporting, and sales performance dashboards. Builds and manages the Sales Operations team, partners with senior leadership and cross-functional stakeholders, drives process improvements, supports business reviews, and advances AI-enabled analytical practices across the sales organization.

Develops product training curricula and digital learning content across instructor-led, virtual, and web-based formats. Partners with subject matter experts and stakeholders to translate complex product and business needs into effective educational materials. Creates tutorials, modules, certifications, simulations, assessments, and knowledge checks using online learning tools. Evaluates and maintains content for accuracy and quality while improving learning processes, tools, and delivery at scale.

Provides first-line technical support for Duck Creek applications, including incident response, troubleshooting, performance monitoring, log review, patching, and deployment assistance. Documents incidents and resolutions, escalates complex issues, collaborates with development and infrastructure teams, supports SLA compliance, and identifies recurring issues and system improvements. Requires a bachelor's degree and 1–2 years of application support or IT operations experience.

Owns and improves the end-to-end software release system across engineering. Responsibilities include managing release calendars, branching strategies, code freezes, readiness checkpoints, deployment coordination, risks, dependencies, stakeholder communications, release governance, and post-release follow-up. The role partners with DevOps and engineering teams to improve CI/CD pipelines, build stability, deployment reliability, automation, and release cadence in a cloud-based SaaS environment.

Owns end-to-end response to high-impact incidents in a 24/7 SaaS production environment. Coordinates technical teams during live incident bridges, removes blockers, manages escalations, communicates business impact, and ensures safe mitigations. Leads post-incident reviews, tracks metrics, improves runbooks and processes, and supports change and problem management. The role requires ITSM expertise, cloud and DevOps knowledge, AI-tool proficiency, strong communication, and calm decision-making under pressure.

Owns the strategy, roadmap, and execution of an agentic AI application that automates insurance product configuration. Translates carrier needs, AI accuracy requirements, document-processing challenges, and scalability goals into product priorities and measurable outcomes. Partners with engineering, architecture, data science, AI platform, customer-facing teams, and carriers to deliver reliable document ingestion, extraction, knowledge graph, validation, and human-review capabilities. Defines evaluation methods, adoption and accuracy metrics, release plans, and responsible AI controls.

Leads planning and roadmap execution for data orchestration capabilities across ingestion, transformation, routing, synchronization, monitoring, and delivery. Defines requirements for reusable pipelines, data contracts, schemas, lineage, observability, and AI-ready data flows. Partners with engineering, architecture, data, security, customer teams, and stakeholders to prioritize capabilities, improve reliability and adoption, reduce custom implementations, and establish measurable outcomes for data trust and onboarding efficiency.

Owns the strategy and roadmap for semantic insurance data products supporting AI, analytics, automation, and enterprise applications. Defines canonical concepts, relationships, metadata, data quality standards, APIs, knowledge graphs, and AI-ready consumption patterns. Partners with engineering, architecture, data, AI, UX, customer success, and insurance stakeholders to translate complex P&C insurance needs into product capabilities, prioritize use cases, establish requirements, drive adoption, and measure business impact.