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
- Solid 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