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.