Product Analyst, Data Platform and Analytics

The audit & assurance profession is the foundation of trust in our financial system and global economy—an essential mission undergoing profound transformation. In a rapidly evolving technology landscape, accountants require world-class tools to enhance trust, drive efficiency, strengthen their capability, and elevate their impact. 

At Caseware, we are at the forefront of this transformation, defining and building the future of accounting technology. We seek a Product Analyst, Data Platform and Analytics to play a critical role in advancing our metric driven culture within our Product Organization. 

You will work closely with Product, Design, Finance, Commercial and Engineering Teams to define success metrics, build analytical frameworks, co-design experiments, and ensure data-driven decision-making is at the core of our product development process. 

❗ This is a full-time permanent position 

❗ This is a new vacancy  

• Own end-to-end product analytics across the full data stack — from pipeline work in our data lakehouse (Microsoft Fabric / Delta Lake) to insight delivery — ensuring data is clean, trusted, and decision-ready. 

• Conduct deep AI trace analysis using Langfuse and similar observability tooling to surface patterns in model behavior, latency, quality, and user interaction across AI-powered features. 

• Build and own funnel analysis across the customer lifecycle — activation, adoption, engagement, retention — identifying drop-off points and quantifying the impact of product changes. 

• Define, track, and report on core product and revenue metrics including NPS, ARR per user, active users, feature adoption rates, and firm-level engagement signals. 

• Build dashboards and scalable reporting in Power BI (Microsoft Fabric) that unify data across systems and make insights accessible to the full organization. 

• Go beyond surface metrics own the "so what," synthesizing data into clear, opinionated recommendations that influence product prioritization and roadmap decisions. 

• Partner with Product Managers to validate hypotheses, run experiments, and measure feature performance with statistical rigor. 

• Tackle data quality head-on diagnose gaps, inconsistencies, and structural issues across our data estate, and work with engineering to close them systematically. 

• Navigate ambiguity confidently  work with incomplete, inconsistent, and loosely structured data and still produce reliable, high-trust outputs that teams can act on.