This is a candidate dataset for the ICP from report №5 — the sixth and final role of six. This is not an assumed gap but a measured one: per Brighty's own team data, the "Data / Analytics" function = 0 current, 1 departed (100% attrition), and the Data Analyst vacancy was opened and closed twice. After the round, investors demand unit economics, retention cohorts, CAC/LTV and the product funnel — without an analyst, B2C growth under a geo-gate runs blind. We assembled a live, verified, contact-ready pool of product/growth analysts from fintech, crypto and CIS-tech donor companies — focused on the exact level Brighty actually needs. Not a raw list — data engineering built to the brief: who, why now, how we found them, and — honestly, who we flagged for review.
prepared 2026-06-28 · MEASURED · Outricher data
In report №5 and the team breakdown (report №2), the gap is measured, not assumed: the "Data / Analytics" function = 0 current employees, 1 departed — the only analyst in company history left and was never replaced (100% attrition). Across the hiring history (68 postings), the Data Analyst vacancy was opened exactly 2 times — following the same "hard to fill" pattern as MLRO (×6) and CISO (×4). The need is recognized but structurally unresolved.
An important honest correction to the ICP: the Growth function at Brighty is NOT empty — Marketing/Growth = 3 current people (0 attrition). The real gap is specifically the analytical data owner, not a "head of growth". So the role is positioned as a product/growth analyst who delivers the numbers to the Growth and product teams — retention, unit economics, CAC/LTV, the funnel — not as a new head of growth. The round context (Series A, $10M) is presented as an external fact (in our team data, funding = null), not as our own measurement. Below: 1,372 analysts already working at Brighty's donor companies, named and with contacts.
Current Data / Product / Growth / BI Analysts, Analytics Engineers and Head/Lead of Analytics at Brighty's donor companies — fintech, crypto and CIS-tech (Revolut, Vinted, Nexo, Bitpanda, Paysera, kevin., Nordigen, TransferGo + the Yandex/Sberbank/BTSDigital data schools, whose graduates have already relocated to Brighty's hubs), in the geography where Brighty actually hires (EU/EEA + Switzerland + UK + UAE; LT/LV/EE and Dubai are its hubs). This is an honest "floor" for the named set of companies — the full graph is larger.
| Donor company | Analysts (current, in hiring zone) |
|---|---|
| Revolut | 806 |
| Vinted | 248 |
| Yandex | 148 |
| Sberbank | 79 |
| Nexo | 27 |
| Bitpanda | 23 |
| TransferGo | 15 |
| kevin. | 13 |
| Nordigen | 6 |
| BTSDigital | 3 |
| Paysera | 3 |
| Wirex | 1 |
| Total (before quota and deduplication) | 1,372 |
| Donor | In dataset (actual) | Share |
|---|---|---|
| Revolut | 93 | 25% |
| Vinted | 84 | 23% |
| Yandex | 79 | 21% |
| Sberbank | 56 | 15% |
| Nexo | 21 | 6% |
| Bitpanda | 17 | 5% |
| TransferGo | 7 | 2% |
| kevin. | 6 | 2% |
| Nordigen | 4 | 1% |
| Paysera | 1 | 0% |
| Wirex | 1 | 0% |
| BTSDigital | 1 | 0% |
da_current_status fields and segments are in the dataset.Brighty needs a working analyst, not a data team built around one. So the main bet is the rising product/growth analyst (budget-friendly, high ceiling). A ready Head/Lead is offered as a separate tier.
Data / Product / Growth / BI Analyst with 2–4 years in a fintech, crypto or marketplace team — already past junior, strong SQL + Python, started picking up dbt and product analytics (Amplitude / Mixpanel), grew fast (junior→middle in < 2 years, visible from position dates across a depth of 5–13 roles). High ceiling, low current cost, hungry for a role with more responsibility and direct access to C-level — all of which Brighty (a flat function, measurably down to one person) has in abundance. This is the direct answer to the gap: delivers numbers to the Growth and product teams immediately. Of these, 107 have a confirmed SQL + Python core, 63 show a clear trace of product methodology (retention / cohort / funnel / A-B).
| Candidate | Current role | Donor | Geo | Stack core | Score |
|---|---|---|---|---|---|
| Syed Ghazanfer Rabbani | Analytics Engineer Amplitude / Mixpanel dbt / BigQuery fintech domain | Revolut | UAE | SQL + Python | A+ 16 |
| Peter Berna Williams | Data Analyst (Product) Amplitude / Mixpanel dbt / BigQuery fintech domain | Revolut | Spain | SQL + Python | A+ 15.5 |
| Carlos Tapia García | Data Analyst Amplitude / Mixpanel dbt / BigQuery fintech domain | Revolut | Spain | SQL + Python | A+ 15.5 |
| Daniel Rubas | Lead Business Intelligence Analyst Amplitude / Mixpanel dbt / BigQuery fintech domain | Bitpanda | Austria | SQL + Python | A+ 15.5 |
| Vasilen Tzanev | Product Analyst Amplitude / Mixpanel dbt / BigQuery fintech domain | Nexo | Bulgaria | SQL + Python | A+ 15.5 |
| Rita Margarida Costa | Data Analyst Amplitude / Mixpanel fintech domain | Revolut | Portugal | SQL + Python | A+ 14.5 |
| Mateus Trentz | Data Analyst dbt / BigQuery fintech domain | Revolut | Portugal | SQL + Python | A+ 14.5 |
| Dmitrii Kuznetsov | Middle Data Analyst Amplitude / Mixpanel dbt / BigQuery fintech domain | Yandex | Spain | SQL + Python | A+ 14.5 |
| Aleksas Ramaška | Decision Scientist / Data Analyst fintech domain | Vinted | Lithuania | SQL + Python | A+ 14 |
| Tomás Almeida Borges | Data Analyst Engineer fintech domain | Revolut | Portugal | SQL + Python | A+ 13.5 |
Badges: "Amplitude / Mixpanel" = a product analytics tool was found; "dbt / BigQuery" = modern analytics-engineering stack; "fintech domain" = experience in fintech / crypto / payments. This is not a hypothesis, it is a line in the file. The examples deliberately surface candidates with a product signal (not pure ML).
Senior Data / Product Analyst, Analytics Engineer or Head/Lead of Analytics — already built retention / unit economics in regulated fintech, often as the sole or first analyst. They meet investor requirements immediately after the round and will not require building a data team around them — someone who has already survived in "solo analyst" mode will not burn out doing the same at Brighty. Of these, 87 are at Senior / Lead / Head analytics level. More expensive, but this is buying readiness, not potential.
| Candidate | Current role | Donor | Geo | Stack core | Score |
|---|---|---|---|---|---|
| Tomas Peluritis | Head of Data Amplitude / Mixpanel dbt / BigQuery fintech domain | kevin. | Lithuania | SQL + Python | A+ 19 |
| Pavel Kudrautsau | Head Of Data dbt / BigQuery fintech domain | Paysera | Lithuania | SQL + Python | A+ 18 |
| Pavel K. | Principal Data Scientist & Product Strategist Amplitude / Mixpanel fintech domain | Bitpanda | Spain | SQL + Python | A+ 16.5 |
| Olga Juralevičiūtė | Senior Data Scientist fintech domain | TransferGo | Lithuania | SQL + Python | B 16 |
| Agrita Garnizone | Head of Data fintech domain | Nordigen | Latvia | — | B 16 |
| Jean de Saint Michel | Head of Data Platform & Engineering fintech domain | Bitpanda | UAE | SQL + Python | A+ 16 |
| Robert Wasilewski | Global Head of Analytics Engineering Amplitude / Mixpanel dbt / BigQuery fintech domain | Revolut | Poland | — | A+ 15.5 |
| Joaquim Costa | Lead Data Analyst fintech domain | Revolut | Portugal | SQL + Python | A+ 15.5 |
| Vitalii Radchenko | Senior Data Scientist (Core) dbt / BigQuery fintech domain | Revolut | Spain | SQL + Python | B 15.5 |
| Pavel Yarema | Senior Business Intelligence Analyst dbt / BigQuery fintech domain | Bitpanda | Austria | SQL + Python | A+ 15.5 |
CAVEAT: the senior tier meets investor requirements immediately, but if budget matters more, a rising middle closes the same role for less on onboarding, with the loyalty of having "grown with the company". The tier choice = "readiness now" versus "budget + ceiling".
Readiness to move is detected structurally from career data — not a guess.
192 of 370 have experience in fintech / crypto / payments. That removes 3–6 months of onboarding into KYC/AML metrics, crypto conversions and the EU geo-gate — the analyst immediately understands why cohorts behave the way they do in a regulated crypto-fiat product.
136 are graduates of strong data schools (Yandex / Sberbank / BTSDigital) who have already relocated to LT / LV / AE / EU (filtered on person-level geo, not company country). RU+EN, strong data school, post-relocation switch trigger. Brighty's team already includes KZ/CIS people — the cultural precedent exists.
49 live in LT/LV/EE (Brighty's hubs), 36 in Dubai (the 2nd hub, ×9 of the team). Zero/minimal relocation — the lowest-risk, fastest hire.
46 with product tools (Amplitude / Mixpanel / GA4), 73 with analytics engineering (dbt / BigQuery / Snowflake), 106 with BI (Looker / Tableau). These are the people who build analytics, not just pull reports.
By function within the pool — a structured analytics funnel, not "everyone under the word analyst".
| Function | People | Share |
|---|---|---|
| Data / Product / Growth Analyst (IC) | 169 | 46% |
| Analytics-adjacent (DS / insights / junior) | 106 | 29% |
| Senior Analyst / Analytics Engineer | 59 | 16% |
| Head / Lead of Analytics | 28 | 8% |
| Adjacent (data-alumni, now non-data leadership) | 5 | 1% |
| Adjacent (analytics-transferable) | 2 | 1% |
| Adjacent (data-alumni, now non-data role) | 1 | 0% |
Honest finding: the broad word "analyst" also captures financial / credit / risk / AML / QA analysts — we deliberately cut them with a negative filter (those are different functions, outside this ICP). The "rising product/growth analyst" layer (169 people) is the direct answer to the measured gap.
ds-no-product-signal-review segment — so you can confirm interest before outreach rather than spend a slot blind. The hot shortlist (A+/A = 77) is title-verified analysts (Data / Product / BI Analyst, Analytics Engineer, Head of Analytics), without ML-flagged DS. Of these, 31 carry a clear product signal in the text (tools / method); for the rest it isn't marked in the profile — that's a question for the interview, not grounds to exclude (the field is incompletely populated).The full dataset: JSONL / CSV / Excel + ready-made segments. Every profile — career depth, education, skills, location, contacts (where present), score and tier. Quality is measured, not claimed.
| Quality metric | Value |
|---|---|
| Profiles in the dataset | 370 |
| Fields per profile (min · avg · max · union) | 99 · 143 · 152 · 155 |
| Outreach-ready (email / phone / profile) | 100% |
| Email (combined) | 36.8% (136) |
| With SQL+Python core / domain fit | 107 / 192 |
| Hot A+/A (of which with a clear product signal) | 77 (31) |
| Email from a former employer (total / among hot) | 60 / 33 |
| Flagged for review (DS without product signal) | 121 |
We provide email and phone only from verified sources — where there is none, it's NULL; we do not invent contacts. Stack and method are a signal from the profile text: the absence of a mark ≠ the absence of a skill, so make the final check at the interview. 60 profiles (of which 33 are in the hot tier) are flagged email-domain-mismatch — a contact at a former employer that may bounce; check before mass outreach. This is not a dataset defect but an honest per-row flag.
This is the sixth and final role from report №5 — all 6 of Brighty's key roles now have a verified dataset with scoring, segments and contacts. The same format can be assembled for any role and company: name it, and we'll build it.
Tell us your target company or ICP and we'll deliver the dataset + a short analysis in the same format.
Request your report — hi@outricher.com ↗