Product Operations Director

Dream Big. Go Beyond. Be Unstoppable. About Us

Kyriba is a global fintech leader empowering CFOs and finance teams with cloud-based treasury, payments, and risk management solutions. We serve 3,000+ customers worldwide, managing $15 trillion in payments annually and helping businesses optimize liquidity performance across the enterprise.

We're on a mission to become the most sought-after cloud technology company globally. We think big, innovate relentlessly, and challenge the status quo every day. If you are a problem-solver who’s ready to push boundaries and achieve more than you thought possible-you'll find an exceptional career within an extraordinary business.

Location:

London

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.

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)

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

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…