Marketing Analyst in IT — CIS and Europe market

The median Marketing Analyst salary is $0/mo per Zorky CRM data (0 active jobs). Real 2026 bands: Junior at Russian companies — $700-1,300/mo, Middle — $1,400-2,600, Senior — $2,800-4,500, Lead / Head of Marketing Analytics — $4,500-7,000. At large companies with large marketing budgets and in e-commerce — higher. At international companies on full-remote a Senior — $4,000-7,000+. Pay is lifted by confident Python and automation, expertise in attribution and end-to-end analytics, understanding of acquisition unit economics. A marketing analyst on average earns slightly less than a product analyst, but more than an executing marketer of the same level.

Junior can work with web analytics and ad cabinets, compute basic metrics, build reports. Jump to Middle — confident SQL and Python, independent work with attribution and end-to-end analytics, reporting automation. Senior builds the marketing analytics system, is responsible for the attribution model and channel payback assessment. Career flow: Marketing Analyst → Senior → Head of Marketing Analytics, or transition to Product Analyst, to general data analytics, or to marketing in a managerial role (with a strong analytical base).

Moscow: Junior Marketing Analyst — $700-1,300/mo, Middle — $1,400-2,600, Senior — $2,800-4,500 (at companies with large budgets higher). St Petersburg — similar bands. Minsk / Kyiv — 10-25% below Moscow. Poland — €2,200-4,500 gross. 0% remote: marketing analytics is done well at a distance. International companies hire Russian-speaking Senior Marketing Analysts on full-remote — $4,000-7,000/mo. Russian regions — base lower, but the role is often remote, which opens access to Moscow and international bands.

Top 5: SQL, Yandex Metrika, GA4, Python, Power BI. SQL — mandatory (data on customers, orders, expenses). Web analytics: Yandex Metrika, Google Analytics 4 (GA4) — on-site behaviour, traffic sources, conversions; understanding of end-to-end analytics systems (the "ad → visit → application → sale" link). Ad cabinets: Yandex Direct, VK Ads, Telegram Ads — their interfaces, exports, and APIs. Python (pandas) — mandatory for Middle+: reporting automation, data collection from ad APIs, complex calculations. Excel / Google Sheets — working tool. BI: Power BI, Tableau, DataLens — marketing dashboards. Statistics and A/B tests — testing creatives, landing pages. Marketing metrics: deep understanding of CAC, LTV, LTV/CAC, ROMI / ROAS, DRR, funnel conversion, cohort analysis, acquisition unit economics. Attribution — key expertise: different models (last-click, first-click, linear, data-driven) and their limitations. Soft skills: understanding of marketing and business, ability to explain to marketers and management which channel pays off and why, to translate numbers into budget decisions.

Data Analyst — general-profile analyst: answers business questions across different company directions (see /research/analyst/data-analyst). Marketing Analyst — essentially a Data Analyst specialised in marketing: same base skills (SQL, BI, statistics, Python), but the applied area is customer acquisition, channels, ad campaigns, their economics and payback; they know web analytics, ad cabinets, and marketing metrics deeply. Marketer (targeting specialist, performance marketer, brand marketer) — launches and runs campaigns: configures ads, creatives, channels, is responsible for their work. The marketing analyst at the same time measures and explains the result: computes which channel pays off, where money is lost, which attribution model is correct, and suggests how to reallocate the budget. Roughly: marketer — action, marketing analyst — measurement and conclusions. In practice the roles overlap: a performance marketer computes a lot themselves, and a marketing analyst understands campaigns well. Career flow: people come into Marketing Analyst both from analytics (Data Analyst → marketing specialisation) and from marketing (a marketer who gained SQL / Python / analytics); further — Head of Marketing Analytics, transition to Product Analyst or to a managerial marketing role.

These are the base metrics of marketing economics — the language a marketing analyst speaks with the business. CAC (Customer Acquisition Cost) — the cost to acquire one customer: how much money (advertising + other costs) was spent to get a customer. LTV (Lifetime Value) — how much money a customer brings to the company over their entire lifetime (or profit — then more correctly). LTV/CAC — the key ratio: if a customer brings more than it cost to acquire them, acquisition pays off; a healthy business benchmark is LTV many times higher than CAC. ROMI (Return on Marketing Investment) and ROAS (Return on Ad Spend) — marketing and ad payback: how much revenue or profit came back per ruble invested. DRR (ad spend share) — what share of revenue goes to advertising. Why: these metrics turn marketing from "we spent the budget — something came in" into managed economics: a marketing analyst computes them per channel and campaign and answers the business's main question — which advertising pays off and which burns money, and where to reallocate budget. Subtlety: honestly computing CAC and LTV per channel is hampered by the attribution problem (see the separate question) — so this is not "arithmetic" but analytical work.

Attribution is the distribution of credit for a sale among ad channels and touches. The problem: a customer almost never comes "through one channel". They might have seen an ad on VK, then googled, then clicked on context in Yandex, then come from an email newsletter and only then bought. The question: to which channel do you record this sale? The answer directly determines which channel is considered profitable and where to put budget — so attribution is critical and at the same time genuinely hard. Attribution models: last-click (all credit to the last touch — simple, but undervalues "top" channels that create demand), first-click (to the first touch), linear (equally between all touches), time-decay (with decay), data-driven (the model itself estimates channel contributions by data — more accurate, but more complex). Why it's hard: no model is ideal; touch data is incomplete (part of the user journey is invisible due to privacy, different devices, offline); channels influence each other. So mature companies build end-to-end analytics (link advertising with real sales in CRM), use a cohort approach and incremental experiments (geo-holdouts, channel shutoff and effect measurement). The ability to soberly work with attribution is what distinguishes a strong marketing analyst from a person who just exports ROAS from the ad cabinet.

Yes, 0% of Marketing Analyst jobs are remote or hybrid. Marketing analytics is done well at a distance: work with data, web analytics, ad cabinets, BI, and calls with the marketing team. Russian e-commerce, product companies, agencies, and brands offer office, hybrid, and remote. International companies hire Russian-speaking Senior Marketing Analysts on full-remote — $4,000-7,000/mo. English is needed for the international market; for the Russian one you can start without it. Remote format is convenient for analysts from regions — opens access to Moscow and international bands.

At the top: Yandex, VK, Ozon. Marketing Analysts are needed everywhere there's a notable marketing budget. E-commerce and marketplaces: Ozon, Wildberries, Yandex Market, SberMegaMarket — large acquisition budgets, analytics critical. Large product and tech companies: Yandex, VK, Avito. Fintech and banks: T-Bank, Sber, Alfa-Bank — competitive acquisition market. Foodtech, travel, edtech (Skyeng, Skillbox etc.), services and mobile apps, gaming (user acquisition — a separate developed area). Brands and retail with their own digital marketing. Performance agencies and analytics agencies. International companies — hire Russian-speaking Senior Marketing Analysts on full-remote. Demand is sustained: the more expensive customer acquisition, the more an analyst is needed who shows what pays off.

People come into Marketing Analyst by two paths — from analytics (adding the marketing domain) and from marketing (adding analytical skills). Roadmap: 1) SQL — master confidently (data on customers, orders, expenses). 2) Web analytics — Yandex Metrika and GA4: traffic sources, conversions, goals, events; understand the idea of end-to-end analytics. 3) Ad cabinets — get to grips with Yandex Direct and VK Ads at least at the reports and exports level. 4) Marketing metrics — learn and correctly compute CAC, LTV, LTV/CAC, ROMI / ROAS, DRR, conversions, cohorts, acquisition unit economics. 5) Attribution — understand attribution models and their limitations; this is the key topic of the profession. 6) Excel / Google Sheets and BI tool — marketing reports and dashboards. 7) Python (pandas) — don't postpone: reporting automation, data collection from ad APIs. 8) Statistics and A/B tests — testing creatives and landing pages. 9) Portfolio — analyse a project's marketing (can use learning data): compute metrics by channel, build a dashboard, give budget recommendations. Resources: marketing and web analytics courses (Yandex, Karpov.Courses, MyAcademy etc.), free — Yandex Metrika and GA4 help docs, unit economics and attribution materials, marketing analyst communities. A strong advantage — marketing experience plus upgraded analytics (or vice versa).

0 active open Marketing Analyst positions in the Zorky CRM sample. The real market is wider: the role is named differently — "marketing analyst", "web analyst", "advertising analyst", "performance analyst", "CRM analyst" — and part overlaps with performance marketing; exact-term search doesn't catch everything. Geography: Russia / remote / Belarus. Sources: hh.ru, Habr Career, getmatch, LinkedIn, Telegram (marketing and web analytics communities, job channels). Demand is sustained, especially in e-commerce and at companies with large acquisition budgets. NB: the Analyst / BI direction historically had difficulties with automatic job classification — the visible number may understate the real market.

A Senior Marketing Analyst is responsible for the company's marketing being managed by data. SQL and Python: expert SQL, confident Python (pandas) — reporting automation, data collection from ad APIs, complex calculations. End-to-end analytics: ability to link advertising with real sales in CRM, build or develop an end-to-end analytics system, understand where data is lost. Attribution: deep understanding of attribution models and their limitations, choice of an adequate model, incremental approaches to channel contribution assessment (geo-holdouts, channel shutoff experiments). Unit economics: confident computation of CAC, LTV, LTV/CAC, ROMI per channel and segment, understanding which customer and channel are really profitable. Cohort analysis: traffic quality assessment by cohorts, not just one-time ROAS. Statistics and experiments: correct testing of creatives, landing pages, channels. BI and visualisation: marketing dashboards actually used for budget decisions. Marketing understanding: know how channels and campaigns work, speak with marketers in the same language. Business thinking: translate analytics into specific decisions — where to move budget, which channel to scale, which to wind down. Communication: explain conclusions to management and marketing and defend them. Mentoring: development of Junior analysts. English — for the international market. The main value of a Senior — make sure the marketing budget is spent on what really pays off and prove it with data.

Data is collected automatically from 1000+ sources — Telegram job channels and job boards across CIS and Eastern Europe (HH, Habr Career, Djinni, DOU, NoFluffJobs, JustJoin.it, Pracuj.pl and others). Parsing runs 24/7, duplicates are filtered by description and URL, salary outliers are stripped. Detailed methodology — on the "How it works" page.