Product Operations & Analytics Director

Kyriba•New York, NY

23h•Hybrid

About The Position

The Director of Product Operations & Analytics is a senior strategic leader who builds and leads the data infrastructure, operational frameworks, and launch operations that enable Kyriba's product organization to make data-driven decisions and operate with world-class excellence. Reporting directly to the CPO, you will be a key member of the Product Leadership Team, partnering with Product Management to translate strategy into operational reality. You will own product performance analytics, R&D efficiency metrics, experimentation frameworks, and the tools and processes that make the product organization highly effective. This is a strategic operational leadership role requiring both analytical excellence and organizational leadership. You will influence product strategy through data insights, drive operational excellence across the product org, and build a high-performing team that serves as the trusted analytics and operational partner to all Product Managers.

Responsibilities

Product Analytics & Insights (30%)
Build world-class product analytics capability
Establish comprehensive product performance dashboards for PMs, executives, and Board
Implement and manage product analytics platforms
Design and maintain data pipelines from products to analytics platforms
Ensure data quality, accuracy, and governance across all product metrics
Build self-service analytics capabilities for product teams
Partner with Engineering on instrumentation strategy and data collection
Define and track strategic product metrics
Product adoption and usage: DAU/MAU, feature adoption, engagement, stickiness
Customer health: Product-driven retention indicators, expansion signals, churn predictors
Business impact: Bookings attribution by product/feature, revenue influence, product-led growth
Quality metrics: Bug rates, performance (latency, uptime), reliability, customer-reported issues
Velocity metrics: Release frequency, time to market, deployment success rates
Commercial analytics and business insights
Track TAM/SAM/SOM penetration by product and segment
Analyze bookings attribution to understand which products and features drive revenue
Identify retention drivers through cohort analysis and feature correlation
Model pricing sensitivity and packaging effectiveness
Support business case development with data and financial modeling
Measure product-led growth (PLG) metrics and conversion funnels
Customer analytics
Analyze customer usage patterns and behavioral segmentation
Identify expansion and upsell opportunities through usage data
Track customer health scores and at-risk indicators (churn prediction)
Measure time-to-value and activation metrics
Support customer research with quantitative data insights
Create customer cohort analyses (by segment, acquisition date, size)
R&D Efficiency & Productivity (20%)
Drive R&D operational excellence
R&D to ARR ratio: Industry benchmarking and optimization
Development velocity: Story points, cycle time, throughput
Feature delivery: Time from ideation to GA, release frequency, scope vs. plan
Resource utilization: Engineering capacity allocation (features vs. tech debt vs. bugs vs. support)
Cost per feature: Understanding development costs and ROI by initiative
Technical debt: Tracking, trending, and impact on velocity
Engineering productivity analytics
Sprint velocity and predictability trends by team
Backlog health and aging analysis
Cross-functional dependencies and bottleneck identification
Release frequency and quality metrics (defect rates, rollback rates)
Technical debt tracking and reduction progress
Engineering satisfaction and capacity metrics
Portfolio optimization
Product portfolio performance analysis (which products drive growth, margin)
Investment allocation recommendations (where to invest R&D resources for maximum return)
Build vs. buy vs. partner analysis with data
Sunset and rationalization recommendations based on usage and business impact
Experimentation & Testing Framework (15%)
Build experimentation culture and infrastructure
Design and implement A/B testing and experimentation framework
Establish statistical rigor and best practices for product experiments
Build tools and processes for running experiments (test design, analysis, learning capture)
Train product teams on experimentation methodology and statistical literacy
Track experiment portfolio, results, and learnings
Support data-driven decision making
Help PMs design statistically valid experiments
Provide statistical analysis and interpretation of results
Build feedback loops to improve experimentation practice
Track success rates and ROI of experiments
Create experimentation playbooks and templates
Evangelize experimentation culture across product org
Tools, Processes & Launch Operations (25%)
Product management tools & systems
Own and manage product management tool stack: Roadmap & backlog tools: Aha!, ProductBoard, Jira, Azure DevOps Analytics platforms: Amplitude, Mixpanel, Tableau, Looker, PowerBI Collaboration tools: Confluence, Miro, FigJam, Notion Customer feedback: Pendo, UserVoice, Zendesk integration Data platforms: Snowflake, BigQuery, data warehouse
Ensure tools are integrated, adopted, and delivering value
Optimize tool spend and vendor management
Drive tool adoption and training
Process optimization and operational excellence
Design and implement product operating rhythms (planning cycles, QBRs, sprint reviews)
Create templates and frameworks for PMs (business cases, PRDs, launch plans, experiment designs)
Standardize product processes across Platform and Applications teams
Drive continuous improvement and efficiency gains
Eliminate friction and waste in product workflows
Document best practices and operational playbooks
Launch Operations & Enablement
Create and maintain launch playbooks and tier framework (Tier 1/2/3)
Facilitate cross-functional launch coordination for major releases
Track launch readiness across functions (Sales, CS, Support, Operations)
Measure launch effectiveness and drive improvements
Provide program management support for Tier 1 launches
Note: PMs still own launch strategy, content, and enablement delivery
Best practices and enablement
Document product management best practices and standards
Train PMs on tools, processes, and analytical frameworks
Share learnings and insights across product org through regular knowledge sharing
Champion data-driven culture and operational excellence
Create onboarding programs for new PMs and POs
Strategic Insights & Executive Leadership (10%)
Executive and board reporting
Prepare quarterly product performance reports for executive team and Board
Create executive dashboards with key product and business metrics
Synthesize insights and recommendations from data
Support CPO with data for strategic presentations and decisions
Track and report on product org OKRs and KPIs
Strategic analysis and influence
Provide analytical support for strategic decisions (build vs. buy, market entry, pricing experiments)
Conduct deep-dive analyses on critical product questions
Model scenarios and forecast outcomes (what-if analysis)
Support product strategy with data validation and market sizing
Identify trends and patterns that inform product strategy
Influence CPO and Product Leadership Team decisions through insights
Cross-functional partnership
Partner with Finance on revenue forecasting and modeling
Collaborate with Sales Ops on bookings attribution and pipeline analysis
Work with Customer Success on health scores and retention analytics
Support Marketing with product-led growth insights and conversion funnel analysis
Partner with Engineering on productivity metrics and resource optimization