Job Description

Tango is a successful, marketleader, a live-streaming Platform with 450+ Million registered users, in an industry projected to reach $240 BILLION in the next couple of years.

The B2C platform, based on the best-quality global video technology, allows millions of talented people around the world to create their own live content, engage with their fans, and monetize their talents.

Tango live stream was founded in 2018 and is powered by 500+ global employees operating in a culture of growth, learning, and success!

The Tango team is a vigorous cocktail of hard workers, creative brains, energizers, geeks, overachievers, athletes, and more. We push the limits to bring our app from “one of the top” to “the leader”.

The best way to describe Tango's work style is not to use the word “impossible”. We believe that success is a thorny path that runs on sleepless nights, corporate parties, tough releases, and of course our users' smiles (and as we are a LIVE app, we truly get to see our users all around the world smiling right in front of us in real-time!).

Do you want to join the party?

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)

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.

Life at the office:

We're based in Limassol, and the team actually spends time together outside of work:

• Volleyball and padel — we play regularly, and not just a couple of us.
• Lunches and dinners together often enough that it's a habit, not an event.
• Analytics breakfast once a month — the whole data team, out of the office, talking shop and whatever else.
• Product offsites once or twice a year — somewhere outdoors, real brainstorming, not trust falls.
• A games floor in the office — gets used.

It's a real team, not a team-building slide.

What success looks like in 12–18 months:

A strong hire will own one or two product domains end-to-end, have shipped analysis that visibly changed the roadmap, and be trusted to take on scope that today sits with me — managing projects, people, and stakeholders on their own authority. There's no rigid career ladder here; responsibility gets handed over when you've earned it, which in practice is faster than most places.

#LI-ID1