Product Analyst

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 300+ 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”. And 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

• Ownership. Be the analytics counterpart for a product domain 1:1 chat, content packs, negotiations, live streaming — you become the person PMs and engineers come to for any number in that area, and the person accountable when it's wrong.
• Experiments. Run A/B tests on product features, end to end. Turn a product hypothesis into a test: primary and secondary metrics, the minimum effect worth detecting, and the audience. Confirm the population split and assignment events are genuinely correct before trusting the readout. Monitor it while it runs, then deliver a clear ship / don't-ship call and the reasoning behind it. This is a large part of the job, not an occasional project.
• Pipelines. Build and maintain the datasets behind your domain Write production transformations as SQL and YAML pipeline config, with freshness, uniqueness and validity checks. Your work goes through code review like any engineer's.
• Events. Specify instrumentation before it's built Write the BI event spec for new features across Android, iOS and Web; then verify after release that events actually fire the way the spec said, and chase the gaps when they don't.
• Dashboards. Own dashboards and the models under them. Including retiring the ones nobody opens. A dashboard with no reader is a cost, not an asset.
• Analysis. Investigate what moved and why. Revenue and conversion anomalies, feature performance, behavioural questions from PMs. Present findings to people who will act on them.
• Incidents. Size production incidents under time pressure. When something breaks, you're asked how many users were affected and for how long — quickly, and precisely enough to defend the number when engineering disagrees with it.
• Cost. Keep query and pipeline cost honest. Partitioning, clustering, and knowing why a query scans what it scans. Warehouse capacity is shared and finite; an expensive pipeline is everyone's problem.
  
  
  

Requirements

• Advanced SQL, including window functions and multi-source joins — and the ability to reason about what a query costs , not only whether it returns.
• A strong experimentation background. Hypothesis testing, confidence intervals, power and minimum detectable effect — and the failure modes: peeking at a running test, multiple comparisons, hunting for a segment where the result works. You should also be the kind of person who checks that the experiment itself is sound before trusting its numbers.
• 3+ years as a product or data analyst on consumer products, with real exposure to both mobile and web.
• Critical use of AI analytics tooling. We use an internal Claude-based layer over BigQuery heavily. We want someone who checks what it produces before passing it on.
• Comfort shipping code: git, merge requests, reading and reviewing someone else's SQL. You'll write production pipeline code, not only notebooks and queries.
• Working knowledge of product event tracking: what a well-specified event looks like, and why the same event behaves differently across platforms.
• English at B1 or above. You'll write specifications that engineers build from and present findings to product stakeholders, so clear written English matters more than fluent speech.
  
  
  

Strong advantage:

• BigQuery specifically — partitioned tables, clustering, slot-based pricing.
• Airflow, dbt, or another orchestrated transformation framework.
• Having been the analyst on a production incident, not only in the post-mortem.
  
  
  

Nice to have:

• Python with the usual statistical libraries — pandas, scipy, stats models.
• A BI tool such as Looker, Tableau, or Power BI — having built the models and dashboards, not only read them. We use Looker today but are moving off it, so the particular tool matters much less than having done the work in one.
• Data quality tooling such as Soda or Great Expectations.
• Experience in social, live-streaming, gaming or another engagement-driven consumer product.