This role operates as an internal data service: cross-functional in scope, outcome-oriented in execution, and strategic in ambition. You will not just analyse existing data – you will help build the integrations and reporting layer that make data a genuine asset at Scompler, collaborating closely with Engineering on infrastructure and with Product leadership on strategic priorities.
Your Mission:
Transform Scompler from a data-rich organisation into a data-informed one. Connect product usage, commercial data, and qualitative signals into a single source of truth. Make that data accessible, trustworthy, and actionable – for every team, at the right moment.
In your first year, success means: audit our current data landscape and tooling, understand the top business questions across departments, build reporting for the key initial needs (product analytics, customer health, commercial segmentation), and create a roadmap for the data infrastructure we need going forward.
- Consolidate data from product, commercial, and customer-facing sources into one integrated, reliable foundation
- Assess and recommend BI tooling that fits our stage and enables self-service exploration across teams
- Improve event tracking quality in the product together with Engineering to ensure clean, structured, and reliable behavioral data
- Develop churn intelligence: identify behavioral patterns in churned accounts and translate them into early warning signals that enable proactive customer outreach
- Build product analytics that correlate feature adoption with retention and expansion outcomes – replacing proxy metrics with evidence-backed prioritization<
- Create commercial intelligence by combining product usage and commercial data to segment the customer base by maturity, industry, and use case – uncovering market potential
- Own the data layer for customer health and QBRs: provide a shared, reliable view per account that Customer Success and Sales can use for internal planning and customer-facing reviews
- Manage a transparent intake backlog for data requests across all departments – prioritize against company goals and deliver with clear output
- Advise teams on how to frame data questions, interpret findings correctly, and draw sound conclusions
- Drive data fluency across the organization together with leadership: establish shared metric definitions, document logic, and build toward self-serve capability so that standard questions can be answered without routing through you
- Contribute to reproducible reporting views for GM, investor, and finance reporting
