Product Analyst

Responsibilities

• Embed in feature work start to finish. Bring a data point of view to how a feature is shaped, then measure whether it actually worked
• Design and run experiments. Build a repeatable testing methodology you can stand behind, including quasi-experimental designs (like interrupted time series) for when a clean A/B test isn’t possible
• Own funnel and journey analysis.

  Find and fix conversion bottlenecks, and maintain the event tracking behind them, instrumenting events, not just consuming them

• Build the foundations yourself. Help set up the structure for how we track data, and write and own your own analytics pipelines and data models on the lakehouse, you’re hands‑on, not waiting on someone else to build every input first
• Partner with Product Managers on impact. On success metrics, ROI, and reporting you work hand in hand with PMs: you bring the measurement and the methodology, and together you turn it into decisions the business acts on
• Measure our internal tools.

  Find out whether the tools and backoffice we build for internal use are actually used, and whether they work

• Build self‑serve tooling. Dashboards and tooling that let teams answer their own questions, so good insight doesn’t bottleneck on one person

Desired Qualities

A bias for autonomy and automation: you scope and drive your own work, and automate the repetitive parts.

If you don’t check every box, we still encourage you to apply. At TBAuctions, we value eagerness to learn and grow more than just ticking all the qualifications.

Qualifications

• BI tools that people actually use (Power BI, Databricks Genie / AI-BI)
• Product analytics domain sense above all: you know how and what to build, you bring best practice to the team, and you share what you know
• Behavioural and event analytics experience (Segment or equivalent): you instrument events rather than only consuming them
• Solid experimentation and statistics: including designs for when a clean A/B test isn’t possible
• Communication that holds the room with developers and C-level alike, and flexes to the audience without losing the point
• Strong SQL and Python: comfortable in a Databricks and dbt environment

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