Source description
About the role
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.
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