What you will be doing:

• 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.

What you will bring:

• 3+ years in product, business, or data analytics in a SaaS or technology environment.

• Solid grounding in SQL and Python — comfortable enough to write queries, wrangle data, and work with APIs, even if AI tools do some of the heavy lifting. What matters is that you can read, validate, and direct the output, not just run it.

• Hands-on experience with data lakehouse platforms (Microsoft Fabric, Databricks, Snowflake, or similar); able to work with engineering teams to improve pipeline quality and schema design.

• Experience with BI and visualization tools; Power BI and Microsoft Fabric are a strong plus.

• Familiarity with product analytics platforms (Pendo, Amplitude, Mixpanel, or similar) and AI observability tools such as Langfuse.

• Proven ability to build funnel analyses, cohort studies, and retention models — not just report numbers, but explain what they mean and what to do about them.

• Comfortable operating in messy data environments — you know how to assess data quality, make defensible assumptions, and communicate confidence levels clearly.

• A habitual AI user — you reach for AI tools as a default, not an afterthought. You use them to accelerate analysis, pressure-test logic, generate hypotheses, and move faster without cutting corners on rigor.

• Curious about how AI systems behave and genuinely interested in measuring them — trace analysis, quality evals, and model observability feel like interesting problems, not overhead.

• Strong data storytelling skills — you can translate a complex analysis into a tight narrative for a non-technical audience without losing the signal.

• Self-starter with high agency; comfortable driving workstreams independently in fast-moving, ambiguous environments.