Analyst / BI: market

Vacancy

an open job that cleared the quality gate and lists at least two technologies

Observed candidate

a candidate whose stack contains this technology; an aggregate — not a single record leaves the perimeter

Matchable candidate

not counted yet

Window

jobs open at the moment the snapshot was built

Top-5 technologies in Analyst postings: sql, cloud, user stories, salesforce, java. Universal must-know — SQL (PostgreSQL/ClickHouse/Snowflake/BigQuery, JOINs, window functions, CTEs, performance). Python for analysis (pandas, numpy, matplotlib, seaborn, scipy.stats). Excel/Google Sheets — ad-hoc work and stakeholder presentations. BI: Tableau, Power BI, Looker, Metabase, Superset, Yandex DataLens (CIS stack). Product analytics: Amplitude, Mixpanel, GA4, Heap, PostHog. A/B testing: statistics knowledge (p-values, power, MDE, sequential testing) + platforms (Optimizely, VWO, Statsig, in-house). For Business Analyst — BPMN/UML, Confluence, Jira, Visio/draw.io. For Systems Analyst — REST/SOAP, Swagger/OpenAPI, Postman, Kafka/RabbitMQ contracts. Analytics Engineer — dbt + git + DWH + semantic layer.

These three roles are often confused but they work on different problems. Data Analyst (DA) — a technical role: SQL extracts, dashboards, ad-hoc research on business requests. Focus on how to get the data and what it says. Stack: SQL + Python + BI. Business Analyst (BA) — a business-oriented role: requirements gathering, BPMN process diagrams, feature specs, stakeholder communication. Focus on what to build and why. Smaller technical stack (though SQL is valued); communication, English, and process modelling matter more. Product Analyst (PA) — a hybrid of DA and product management: A/B tests, product hypotheses, retention/LTV/cohorts, funnel analysis. Focus on which product features work and why. The most premium analytics segment by pay in product companies. Averages: PA > BA ≈ DA in product companies; BA > DA in enterprise/banks.

Yes — analysts are one of the most remote-friendly IT specialisations: 29% of jobs are fully remote or hybrid. The work is data and tooling driven; physical presence is rarely required. Fully remote roles: product startups, international SaaS companies, CIS-relocant teams (Dubai, Cyprus, Lisbon, Tbilisi, Yerevan). Hybrid (1–3 days in office): large IT, banks, telecom, retail. Office-only: most commonly Business Analyst in traditional banks with compliance requirements and Systems Analyst in large enterprise projects. English opens up the global remote market with a 20–40% pay premium.

SQL is mandatory for any analyst (DA/PA/BA at Middle+). Without SQL you will not land a job — it is the baseline skill on 100% of postings. Python is must-have for Data/Product Analyst at Middle+ (pandas, data cleaning, basic ML, routine automation). For Business Analyst Python is a plus but not required. What to know in Python for analytics: pandas (DataFrame, groupby, merge), numpy, matplotlib/seaborn for visualisation, basic statistics (scipy.stats), Jupyter notebooks. What you do not need: advanced OOP, web-development frameworks, DevOps. Programming in analytics is a tool of inquiry, not the product itself. Analytics Engineer is the exception: dbt + Jinja templating + git workflows plus solid programmatic thinking move the role closer to Data Engineer.

Analytics is one of the most accessible entry points into IT. Optimal roadmap: 1) SQL to a confident level — SELECT, JOIN, GROUP BY, window functions, CTEs. Courses: Stepik «SQL для всех», SQL-Academy, Mode Analytics SQL tutorial, LeetCode Database. 2) Excel/Google Sheets — pivot tables, VLOOKUP/XLOOKUP, basic formulas. 3) One BI tool: Tableau Public (free) or Power BI Desktop. Build 3–5 dashboards on open data. 4) Python pandas — Kaggle tutorials, pandas docs, "Python for Data Analysis" by Wes McKinney. 5) Basic statistics: confidence intervals, correlation vs causation, A/B tests (p-value, power, effect size). "Statistics Done Wrong". 6) Pet project: end-to-end analysis of an open dataset (Kaggle, data.world) with a dashboard and conclusions on GitHub. 7) Training programs: Karpov.Courses, Yandex.Practicum «Аналитик данных», Skillfactory, Stepik «Python для анализа данных». Time to first Junior role — 4–8 months of full-time study.

As of the latest data refresh, the Zorky CRM sample contains 3934 active open Analyst positions. These are postings published in the last 90 days. Geography: leaders are US, PL, GB. The sample covers all sub-specialisations: Data Analyst, Business Analyst, Product Analyst, Marketing Analyst, Financial Analyst, Systems Analyst, Analytics Engineer, BI Developer. The real market is wider than the sample: many Analyst positions are published only inside major companies' internal HRMS or through closed recruiting channels. Analytics Telegram channels («Аналитика и данные», «Data Driven») are a good additional source for Senior positions.

A Senior Analyst owns the full cycle: from requirements to business conclusion. Technically: SQL at mastery (window functions, optimisation, query plans), Python (pandas + scipy + ETL/report automation), one BI tool deeply (Tableau/Power BI/Looker — calculated fields, LOD expressions, dashboard optimisation). Statistics: A/B-test planning (power, MDE, sample size), diagnostics (SRM, peeking), interpretation (sequential vs fixed-horizon), bootstrap, Bayesian approaches. Product thinking: understanding funnels, retention/LTV/cohorts, unit economics, metrics across each layer of the product. Communication: data storytelling, presentations to C-level, defending conclusions in front of sceptics. Mentorship: SQL/Python code-review for Middle/Junior. English for Senior+ is must-have (even in local roles at large companies, docs and processes are often in English).