Product Operations Specialist

Key responsibilities
• Product analytics — analyse product usage data to understand user behaviour, feature adoption, and usage patterns across SmartStream products; surface insights to product teams and leadership
• Data-driven decision support — design and analyse A/B tests; help product managers frame questions the data can answer and translate findings into recommendations
• Cross-team insight — work across product groups rather than embedded in one; identify patterns, opportunities, and risks that are only visible at the organisation level
• AI adoption analytics — help measure and grow the adoption of AI-assisted development workflows across engineering; build the metrics and feedback loops that support this transition
• Reporting and communication — build dashboards, reports, and narratives that make data accessible to non-analysts; present findings to product, engineering, and leadership audiences
• Engineering analytics (developing) — over time, extend into engineering flow and delivery data (e.g. Jellyfish) as a complement to product insight
• Security tooling analytics (developing) — over time, contribute to analysis of security tooling adoption and effectiveness (e.g. Snyk coverage, remediation trends) as part of the wider engineering enablement picture

Skills & experience
Essential:
• Comfortable working with data — SQL, Python with pandas, or equivalent
• Strong written and verbal communication; able to explain findings clearly to non-technical audiences
• Curious, self-directed, and comfortable working across teams rather than within one

Strongly preferred:
• Some hands-on experience with product analytics tools (PostHog, Amplitude, Mixpanel, GA, or similar)

Developed in role:
• Statistics and experiment design
• Understanding of how agile product and engineering teams work and make decisions
• Interest in AI and how it changes the way software is built
• Exposure to security tooling and metrics

Growth path
The initial focus is product analytics, where SmartStream has a clear unmet need, alongside supporting the organisation's AI-assisted development transition through data. Over time, the role can grow into broader engineering enablement — engineering metrics (Jellyfish), security tooling insight, and cross-cutting analysis of how the organisation delivers software. A strong platform for a career in analytics, engineering effectiveness, or data-informed product leadership.