Lead Product Analyst

About the team and how we work:

The analytics team sits inside a product organization that moves fast. When there's solid data, we use it; when there isn't, we use judgment — and a big part of this role is knowing which mode you're in.

The pace can be intense at times — tough releases, fast reprioritization, stakeholders who need an answer today. In exchange, you get a lot of autonomy over how you work, a flat structure where strong initiatives get picked up quickly, and direct access to senior management when the question calls for it. That's the trade-off, and worth knowing upfront.

Responsibilities

Why this role might actually be interesting:

A few things that tend to matter to strong analysts thinking about a move:

• The product is a real creator economy at scale. Millions of daily users — creators, viewers, chatters, gifters, resellers. The economics are two-sided and emotional, not transactional. Retention of a streamer is a different problem from retention of a viewer, and both are tangled together. There's no playbook for this; you figure it out.

• The data is big and messy in interesting ways. Many terabytes a day flowing into BigQuery. Real-time streaming behavior, gifting flows, multi-region patterns (MENA, India, LATAM behave very differently from Western markets and from each other).

• You can touch any domain where you can bring value. The team is flat enough that if you see a problem worth solving, you go solve it — you don't need to be assigned.

• Direct access to product leadership and senior management once you've earned it. Your analysis lands with the people making decisions, not three layers down.

• A team that learns from each other. Our analysts are strong in different areas — BI, product analytics, experimentation — and we lean on each other's strengths instead of pretending anyone has the full picture. I want to hire someone who'll push the team further on analytics engineering and causal inference, and who'll pick up things from us in return.

• Stock options with a real shot at paying out. Not a lottery ticket — a meaningful part of compensation, and there's a credible path to liquidity.

What you'll work on in the first 6 months:

Real open problems from the current roadmap — not hypotheticals:

• Web → PWA install conversion for new users. What motivates a new web visitor to install the PWA, what drops them off, and what moves the needle? Build the picture, propose experiments, own the follow-through.

• Mobile retention, one level deeper. Primary retention analysis already exists. Your job is to go further — by cohort, acquisition surface, first-session behavior, creator exposure — and turn it into something that reshapes the roadmap, not just a report.

• Marketing × retention by acquisition source. How do long-term retention and performance vary by where the user came from? Build the framework and measurement, not just the dashboard.

• Own and evolve data domains. Build new data sources for emerging product areas; refactor and modernize older ones.

Your insights will go directly to PM leads and product leadership, and will shape roadmap decisions — not sit in a deck.

Requirements

What we're looking for:

• 4+ years as a product or data analyst, ideally on consumer mobile or web products.

• Strong SQL. CTEs, window functions, query optimization, and comfort with partitioning/clustering on large tables. We run on BigQuery.

• Looker — 2+ years hands-on. You can build and maintain LookML, not just use dashboards someone else wrote.

• Analytics engineering mindset. Track record of modeling data well — building reliable, reusable data sources that others in the company can trust.

• Pragmatic use of AI tools. You use modern AI assistants day-to-day — for SQL, analysis, exploration, writing — not as a buzzword, but as a way to ship faster and ask sharper questions.

• Statistics and A/B testing — confidence intervals, hypothesis testing, power, practical vs. statistical significance. You've run real experiments and know the traps.

• Product sense. You ask "so what?" before "how?". You push back on vague requests — and here, PMs will welcome that.

• English: B2 or higher.

Our stack: BigQuery · Looker · Airflow · GCP (incl. Vertex AI)