About the Company
EMEX is an international holding company: a marketplace, information business, fulfillment, and asset management in the Russian Federation, UAE, and Japan. Over 20 years of profitable operation.
In this role, you will work with the EMEX E-com system — a platform that connects buyers and sellers and covers the entire cycle from user acquisition and product selection to order fulfillment, delivery, returns, and repeat purchases.
E-com generates a large amount of data daily about users, orders, product offerings, returns, and user experience quality.
Our task is to integrate this data into a unified system and make the business economics and key processes measurable and manageable.
Responsibilities
Why this role exists
We are looking for a Senior Analyst to strengthen the business and product analytics system of the marketplace.
The goal of the role is to build an end-to-end analytics system that will allow the business to understand how NMV, profitability, and EBITDA are formed, where the E-com system loses users, orders, and money, and which changes will yield the maximum economic effect.
The analyst will work with the entire E2E E-com loop: from traffic, activation, and conversion to orders, product offering, users (suppliers and buyers), fulfillment quality, returns, and financial results.
The main clients for analytics are the CEO, the Head of Product, and the Head of Quality Management.
The analyst's task is not only to show what happened but also to explain why it happened, what it costs the business, and what needs to be changed.
What is within the scope of the role
- E2E Analytics of the E-com Loop
- Information Slice — transitions through statuses and actions within the system throughout the order journey: visits, views, search, cart, checkout, payment, assembly, shipment, delivery, cancellation, return. Example metrics: funnel step conversion, stage duration, share of cancellations and rejections.
- Product Slice — order items: SKU, category, brand, supplier, warehouse, price, delivery time, availability. Example metrics: demand coverage (match rate), out-of-stock rate, return rate by category and supplier.
- Documentation Slice — invoices, UCDs (Unified Control Documents), waybills, discrepancy reports, certificates and declarations, marking codes, claims. Example metrics: percentage of orders closed with documents on time, time to close, revenue volume in unclosed documents, percentage of items without mandatory confirmations.
- Assembly into a Unified System — product listings are linked by a common order and user key, creating an end-to-end funnel from the first visit to repeat purchase and allowing the breakdown of performance drops by cause: process, assortment, or checkout. E2E dashboards for business management are built on this basis.
Result: Management views the marketplace as a single system, understands the dynamics of key indicators, and identifies at which stage and for what reason deviations occur.
- Order Economics and Financial Analytics
- Order and Transaction Economics — what constitutes the result for one order: revenue, fulfillment cost, Emex commission. Example metrics: Cost per Transaction — total costs per transaction (logistics, processing, payment, support).
- Financial Result and its Decomposition — breaking down the outcome to its drivers, not just stating the sum. Example metrics: NMV — Net Merchandise Volume (turnover for fulfilled orders); Returned NMV — turnover returned due to returns and rejections; Total Margin — gross profit after direct costs; EBITDA — earnings before interest, taxes, depreciation, and amortization.
- Unit Economics Model — calculation and development of a model that translates the behavior of one user and one order into the overall financial result.
- Impact of Factors on Economics — how conversion, frequency, average order value, returns, and fulfillment quality change profitability; which factor contributes most to deviations.
- Evaluation of Change Effects — calculating the financial impact of product and operational initiatives, identifying sources of losses and areas for profitability growth.
Result: The business understands order economics and the main drivers of financial results, and initiatives are evaluated by their impact on profitability and EBITDA.
- Support for Management and Product Decisions
- Task Formulation — translating business questions and hypotheses into measurable analytical formulations: what exactly to calculate, on what data, what answer is considered sufficient.
- Pre-Launch Assessment — calculating the potential impact of an initiative: which metric it affects, by how much, and what that means in monetary terms.
- Recommendation — data-driven conclusions with stated assumptions and limitations, not just raw data exports.
- Prioritization — participation in selecting product and operational initiatives based on expected impact and implementation cost.
- Post-Launch Measurement — comparing actual results with forecasts and analyzing discrepancies.
Result: Key decisions are data-driven, initiatives have calculated expected impacts, and after implementation, the business understands their actual results.
- Data and Analytical Infrastructure
- Data Handling — SQL for data exploration, verification, and transformation; Python for modeling and automating regular calculations.
- Unified Definitions — documenting formulas and boundaries for each business metric, ensuring consistency of indicators across reports and sources.
- BI Layer — creating and developing dashboards for regular management: owner, update SLA, deviation analysis procedures.
Result: The business has a single source of analytical information, metric definitions are agreed upon, and analytics are reproducible and scalable.
The role's objective is not to maintain a set of indicators but to connect them with cause-and-effect relationships and economics, so that changes in operational or product metrics translate into an impact on orders, NMV, profitability, and EBITDA.
Requirements
Tech Stack
Storage: Snowflake
Data Sources: MS SQL Server, Postgres, ClickHouse, 1C
BI: Tableau, Metabase
Tools: SQL (primary), Python
Level and Experience
- Senior
- analytics experience — 5+ years;
- experience with e-commerce, marketplaces, or other transactional digital products will be an advantage;
- experience with both product and financial analytics simultaneously will be an advantage.
Hard Skills
- proficient SQL, Python for data analysis, automation, and modeling;
- building business and product metric systems;
- product analytics: conversion, activation, retention, cohorts, frequency;
- financial and business analytics, understanding of unit economics and profitability drivers;
- building BI dashboards, understanding DWH principles;
- experience with Snowflake, MS SQL Server, Postgres, ClickHouse, or similar DBMS;
- ability to independently conduct analytical research from hypothesis formulation to business recommendations.
Soft Skills
- Systems thinking.
- Business orientation.
- Ability to work with uncertainty.
- Independence.
- Prioritization.
- Structured communication.
- Ability to work with a large number of interconnected indicators.
- Ability to influence decisions through data and argumentation, not formal authority.
Conditions
- Employment: Labor Code of the Russian Federation.
- Schedule: 5/2, hybrid (Dzerzhinsky).
- Probationary period: 3 months (goals described above).
