Full-stack developer, data engineer, and entrepreneur with over 20 years of experience. I focus on BI/data-analytics, including AI and use Python everyday as a versatile Swiss Army knife and believe that almost any problem can be solved. --- **Links:** My Services and Case Studies: _Website not available. Sign in: https://www.twine.net/signup_ My Medium Articles: _Website not available. Sign in: https://www.twine.net/signup_ Linkedin: _Website not available. Sign in: https://www.twine.net/signup_ Dune Analytics: _Website not available. Sign in: https://www.twine.net/signup_ --- Founder of SellerFlux, where we empower e-commerce businesses with data-driven solutions and deliver specialized software designed specifically for the e-commerce sector. I open for co-operation. Previously, I played a pivotal role in the growth of Arteza Inc, which was ranked #32 among the fastest-growing companies in the USA in 2020 (Inc. 5000 List), surpassing $100 million in annual sales and securing a spot among the top 50 sellers on Amazon. Now, I live with my family in sunny Portugal, where I enjoy bouldering, surfing and travelling. --- **Employment and project experience** **SellerFlux - Founder and CTO** _Apr 2024 - Present_ Empower E-commerce businesses with Software Development and Business Intelligence. **Lead Data Engineer / BI Analytist** _Sep 2022 - Mar 2024_ Working in Arteza Inc as a Data Engineer and BI Analyst. Supporting the entire data processing architecture (AWS, Airflow, Redshift, etc.) and conducting business analytics (Python, Tableau, etc.) **General Manager** _Jan 2018 - Aug 2022_ In charge of European office (Minsk, Belarus) (170+ employees / 12 departments) of Arteza, high growing (#32 in Inc. 5000) US-based e-commerce Art-Supply Brand. In addition, focused as BI/Marketing Analytics. Participation in some internal projects (using Python, Tableau, ML, and NLP techniques to analyze data for marketing purposes). In 2022 orchestrated the smooth closure of the office in Minsk **Founder CEO** _Jan 2013 - Dec 2017_ Built a successful outsourcing IT company. Growth of up to 40 people. Main activities: software development high-load web-application, development of CRM, and ERP web-based systems.…

Andrei Kushniarou

PRO
5.0 (1 review)

Full-stack developer, data engineer, and entrepreneur with over 20 years of experience. I focus on BI/data-analytics, including AI and use Python everyday as a versatile Swiss Army knife and believe that almost any problem can be solved. --- **Links:** My Services and Case Studies: _Website not available. Sign in: https://www.twine.net/signup_ My Medium Articles: _Website not available. Sign in: https://www.twine.net/signup_ Linkedin: _Website not available. Sign in: https://www.twine.net/signup_ Dune Analytics: _Website not available. Sign in: https://www.twine.net/signup_ --- Founder of SellerFlux, where we empower e-commerce businesses with data-driven solutions and deliver specialized software designed specifically for the e-commerce sector. I open for co-operation. Previously, I played a pivotal role in the growth of Arteza Inc, which was ranked #32 among the fastest-growing companies in the USA in 2020 (Inc. 5000 List), surpassing $100 million in annual sales and securing a spot among the top 50 sellers on Amazon. Now, I live with my family in sunny Portugal, where I enjoy bouldering, surfing and travelling. --- **Employment and project experience** **SellerFlux - Founder and CTO** _Apr 2024 - Present_ Empower E-commerce businesses with Software Development and Business Intelligence. **Lead Data Engineer / BI Analytist** _Sep 2022 - Mar 2024_ Working in Arteza Inc as a Data Engineer and BI Analyst. Supporting the entire data processing architecture (AWS, Airflow, Redshift, etc.) and conducting business analytics (Python, Tableau, etc.) **General Manager** _Jan 2018 - Aug 2022_ In charge of European office (Minsk, Belarus) (170+ employees / 12 departments) of Arteza, high growing (#32 in Inc. 5000) US-based e-commerce Art-Supply Brand. In addition, focused as BI/Marketing Analytics. Participation in some internal projects (using Python, Tableau, ML, and NLP techniques to analyze data for marketing purposes). In 2022 orchestrated the smooth closure of the office in Minsk **Founder CEO** _Jan 2013 - Dec 2017_ Built a successful outsourcing IT company. Growth of up to 40 people. Main activities: software development high-load web-application, development of CRM, and ERP web-based systems.…

Available to hire

Full-stack developer, data engineer, and entrepreneur with over 20 years of experience. I focus on BI/data-analytics, including AI and use Python everyday as a versatile Swiss Army knife and believe that almost any problem can be solved.

Links:
My Services and Case Studies: Website not available. Sign in: https://www.twine.net/signup
My Medium Articles: Website not available. Sign in: https://www.twine.net/signup
Linkedin: Website not available. Sign in: https://www.twine.net/signup
Dune Analytics: Website not available. Sign in: https://www.twine.net/signup

Founder of SellerFlux, where we empower e-commerce businesses with data-driven solutions and deliver specialized software designed specifically for the e-commerce sector. I open for co-operation.

Previously, I played a pivotal role in the growth of Arteza Inc, which was ranked #32 among the fastest-growing companies in the USA in 2020 (Inc. 5000 List), surpassing $100 million in annual sales and securing a spot among the top 50 sellers on Amazon.

Now, I live with my family in sunny Portugal, where I enjoy bouldering, surfing and travelling.

Employment and project experience

SellerFlux - Founder and CTO
Apr 2024 - Present
Empower E-commerce businesses with Software Development and Business Intelligence.

Lead Data Engineer / BI Analytist
Sep 2022 - Mar 2024
Working in Arteza Inc as a Data Engineer and BI Analyst. Supporting the entire data processing architecture (AWS, Airflow, Redshift, etc.) and conducting business analytics (Python, Tableau, etc.)

General Manager
Jan 2018 - Aug 2022
In charge of European office (Minsk, Belarus) (170+ employees / 12 departments) of Arteza, high growing (#32 in Inc. 5000) US-based e-commerce Art-Supply Brand. In addition, focused as BI/Marketing Analytics. Participation in some internal projects (using Python, Tableau, ML, and NLP techniques to analyze data for marketing purposes). In 2022 orchestrated the smooth closure of the office in Minsk

Founder CEO
Jan 2013 - Dec 2017
Built a successful outsourcing IT company. Growth of up to 40 people. Main activities: software development high-load web-application, development of CRM, and ERP web-based systems.

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Skills

Am
Amazon Web Services
Da
Data Science
Da
Data Visualization
Django
Po
PostgreSQL
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Experience Level

Amazon Web Services
Expert
Data Science
Expert
Data Visualization
Expert
Django
Expert
PostgreSQL
Expert
Python
Expert
SQL
Expert

Language

English
Fluent
Russian
Fluent
Belarusian
Fluent
Portuguese
Beginner

Education

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Qualifications

Add your qualifications or awards here.

Industry Experience

Software & Internet, Computers & Electronics, Professional Services
    uniE613 Insight Discovery – Deep Analysis of Customer Behavior
    Deep analysis of customer order data led to valuable insights, enabling the launch of new product bundles and improved marketing strategies. By identifying patterns in product pairings, sequential purchases, and customer behavior, we helped our client enhance their e-commerce performance and better understand their target audience. Prerequisites Our client, an Amazon seller with multi-million dollar sales, consistently analyzed the market and launched new products in the creative goods sector. One approach to finding new products was the introduction of bundles, which are sets of combined items. These bundles could consist of related products that customers often use together, or a product paired with essential supplies for initial use. As order data accumulated, the idea emerged to conduct a deep analysis of this data to identify patterns in what products customers frequently purchase together or subsequently order, with the goal of creating bundles based on these insights. Task Initially, the task was defined as follows: analyze all orders over several years to identify potential bundles. Identify products frequently purchased together. Highlight the most popular pairs and trios, and generate a report showcasing which products are often bought together, their total quantities, and their percentage of overall orders. Identify products that are purchased sequentially. Often, customers do not buy two or three items at once but make additional purchases later. Additional Insights During our testing, we discovered additional insights and proposed creating extra reports: Initial Product Analysis: The first product purchased influences the overall perception of the brand. This report shows the likelihood of customers returning and making another purchase after buying a specific initial product. It highlights which products perform best and, conversely, which ones underperform and damage the brand’s image. Repeat Purchases: Some products are purchased repeatedly by customers, and the proportion of such customers can be significant. This insight can be used to create subscription services for products or bundles containing multiple identical items. Customer Flow Between Categories: Sometimes, after becoming familiar with products from one category, customers start purchasing products from another category within the same brand. Implementation We developed an automated Python script that extracts and groups all orders from various sales channels (Amazon, Shopify, Retail), analyzes all products purchased by a single customer within a single order or across multiple orders, and generates several ready-to-use reports Result The insights gained allowed the Research and Development department to launch new composite products and enabled the Marketing department to improve advertising strategies, ultimately enhancing the client’s business performance.…
    uniE613 High Level Performance Report
    We developed an Automated Performance Report for an e-commerce business, covering sales and inventory data across various channels (Amazon, Website, Retail). The report is updated weekly, with links automatically sent to the company's leadership. Implemented in both Tableau and Google Spreadsheet, the solution offers varying levels of detail and flexible customization. Task We were tasked with creating a high-level performance report to track the sales and business efficiency of Amazon and other sales channels. The report needed to be automatically updated, with a link to the newly generated report sent to the C-level and VC-level executives within the company. The report is SKU-based and weekly-based, meaning it is generated at the beginning of the week and automatically compiled. Two versions of the report were implemented – one in Tableau and another in Google Spreadsheet, each with different levels of detail and customization. Report The report contained the following data and fields (briefly grouped): Product Data: SKU, ASIN, Description, Product Categories, ABC class, first product launch date... Sales Channels: Amazon Third-party (3P), Amazon First-party (1P), Website sales, Retail sales + by regions (US/EU) + overall sales Sales Advanced Statistics: ASP (Average Sale Purchase) CW (Current Week) Sales, CW LY (Current Week Last Year) Sales, CW vs CW LY % Sales ... Rank Sales ... Units Advanced Statistics: CW Units, CW LY Units, CW vs CW LY % Units ... L30D Units... Rank Units ... COGS (Cost of Goods Sold): YTD COGS Rank COGS YTD Product Profit, Rank Product Profit CW, LW Product Margin ... Inventory Information: Inventory on Hand Value On Hand Inventory LW (Last Week) On Hand Inventory LY (Last Year) Inventory Units in different markets Week of Supply Inventory Turns ... Implementation The report data is generated through a single large SQL query in the Data Warehouse and cached in a separate table for fast report retrieval later on. A Python script that triggers the SQL query and compiles the report runs automatically once a week within a reliable data pipeline management system — Airflow. Google Spreadsheet Implementation The Google Spreadsheet implementation is the primary solution. Once a week, a Python script creates a new sheet in the spreadsheet and uploads the report. The columns are automatically formatted and color-coded. After the report is generated, a link to the created sheet is automatically sent to the C-level and VC-level executives. Tableau Implementation The implementation in Tableau is more advanced, with additional filters: Flexible Filters: Allows dynamic changes to the data range (week, month, quarter, year). Different levels: Displays statistics not only at the SKU level but also at the category level. Charts: Displays charts on sales and a range of key performance indicators.…
    uniE613 Amazon Price and BuyBox Tracker
    We developed a solution for a company selling on Amazon to automate the monitoring of competitor prices and Buy Box changes. The system tracks multiple products daily, instantly notifying the team via Telegram when competitors with better prices appear or when products are blocked. This enabled the client to respond quickly to changes and maintain a high level of sales. Prerequisites Our client is a company selling products on Amazon. Since the products are not unique, competitors frequently appeared on the Amazon listing page with better prices, taking over the Buy Box (a section on the Amazon page where there is a direct purchase button; statistically, 80% of sales are made through the Buy Box). Often, the client's employees would only realize the presence of a competitor with better prices and the loss of the Buy Box with a delay (only after noticing a drop in sales). This created the need for an automated process. Objective The task was divided into the following parts: Buy Box Monitoring: If any other company takes over the Buy Box, a notification should be sent. Competitor Quantity and Price Monitoring: If a new competitor appears in the list of sellers or a competitor sets a price lower than the company's product, a notification should be sent. Product Status Monitoring: If a product is blocked or sales are halted, this should also be immediately known, and a notification should be sent. Implementation After testing and research, Keepa service was chosen for monitoring the Buy Box and competitors. Keepa continuously scans Amazon and provides a convenient API at a reasonable cost. The minimum subscription allowed monitoring the necessary parameters for 150 products every hour. For easy management of the product list (ASINs), Google Spreadsheets was chosen. The script automatically downloads the product list from the document before execution, allowing the client’s employees to dynamically change the list of products to be monitored at any time. All logic was implemented using Python in the form of an Airflow DAG, installed on our server. We extensively use Airflow, an open-source workflow management platform that allows the implementation of complex and reliable data processing configurations and, importantly, continuous maintenance. The Telegram bot, which sends messages, was developed by us and maintains a high level of privacy, with no access to our clients' private data (messages, files). It can be added to any internal company Telegram chat and is only capable of sending notifications. Result All goals were met, and a reliable service was developed that efficiently (every hour) monitors anomalies related to prices and the presence of competitors in Amazon listings, sending notifications via Telegram.…
    uniE613 Amazon Vendor (1P) Invoices Automation
    We automated the retrieval of Amazon 1P sales data by developing a Selenium-based script that extracts invoice details from Vendor Central reports. This solution improved our client's ability to manage and analyze sales data across channels, enhancing their planning and analytics. Prerequisites Our client, alongside Amazon third-party (3P) sales, was expanding their sales through the first-party (1P) model, where Amazon directly purchases products in bulk and places orders on behalf of the company. The first-party model differs from the third-party one, is available only to a select group of sellers and products, but offers several advantages and is more profitable. Objective To integrate 1P sales analytics, enhance planning, and compare with other sales channels, it was necessary to obtain data on bulk invoices, specifically how Amazon purchases products. The task was to retrieve a list of invoices, their status, and the products (with their ASINs) included in each invoice. Unfortunately, Amazon does not provide an API for retrieving invoice data. Invoices and their statuses are only accessible directly through the vendorcentral.amazon.com interface. We were tasked with the efficient extraction of data from the Vendor Central interface. Implementation After a series of tests, we developed an automated solution using Selenium. The script emulates user actions daily, autonomously logging into the Amazon Vendor interface and navigating through all invoices to retrieve and update their data for the past 30 days. This solution is implemented for both the US and European markets. The invoice data is directly uploaded into tables in the Data Warehouse, where it is available for reporting and BI analytics. Result Our solution automated the retrieval of invoice data and improved planning and analytics for the new data channel – the first-party Amazon model (1P).…
    uniE613 Retail Data Collection and Weekly Reporting in Tableau
    We developed an automated system for data collection from Walmart, Michaels, and Joann, and created a weekly automated report in Tableau. Prerequisites Our client, an eCommerce company with millions of monthly sales on Amazon, began to expand into retail and took steps to increase sales through retail channels. Previously, specialists from our client's company manually imported data from Walmart, Michaels, and Joann on a weekly basis, processed it, and unified it into a single format to create a weekly report. This resulted in a significant workload for the employee and diverted attention from more important tasks. Additionally, the weekly reports were only created in Excel, were not maintained in a consistent format, making it extremely difficult to analyze and plan sales efficiency over long periods. Task After reviewing the problem, we proposed the development of an automated solution to save reports in their original format from Walmart, Michaels, and Joann systems, followed by automated processing and unification into a single format. We were also tasked with creating a weekly report in Tableau. Solution We implemented a Solution based on Selenium, which emulates user actions in the browser and downloads the required reports weekly for Walmart, Michaels, and Joann with the necessary settings to a storage (Data Lake). Our processing scripts individually processed the original reports for Walmart, Michaels, and Joann, unified all data into a single format, and saved the result to a database (Data Warehouse). The BI connector prepares data from the Data Warehouse for reporting in Tableau. We created a convenient report in Tableau for data visualization. In this report, our clients can analyze weekly sales as well as by SKU, Marketplace, and Average Item Price.…
    uniE613 Customer Behavior Analysis - Support Messages Evaluation
    We Identified Key Insights (By applying Machine Learning and NLP), that helped our client optimize support performance, enhance product quality monitoring, and improve marketing strategies, leading to better customer satisfaction and new product bundles. Prerequisites As part of our quest for insights for our client—a major brand in the creative products sector—we decided to analyze the messages users send to technical support, along with the responses from support specialists. Our expectation was to uncover patterns and insights that could improve the performance of technical support, detect product quality issues early, and ultimately enhance overall business efficiency. Technical Implementation and Testing In the initial testing phase, we developed a script to extract all messages from the technical support system (Zendesk) and processed them using Machine Learning techniques for text analysis (NLP). Additionally, each message was linked to actual orders using client data, identifying the specific products the customer had purchased before contacting technical support, to generate product-based statistics. All messages were categorized into the following two groups: Problem — related to product quality or order issues. Question or suggestion — related to product usage or inquiries about other brand products that could complement the purchased item). We identified the following key metrics: Sentiment Analysis of User Messages: Sentiment scores were on a scale from 1 to 5, where 1-2 indicated negative sentiment, 3 was neutral, and 4-5 indicated positive sentiment. Tone of Support Response and Perceived Resolution: Whether the issue was resolved or not, from the client's perspective. Customer Order History Before and After the Support Interaction: An additional crucial metric for evaluating support effectiveness. Support Response Time and User Satisfaction: Measuring the speed of response and the user’s feedback on the support experience. Common Phrases and Topics: Identification of typical issues in customer inquiries or suggestions. Insight Discovery and Reporting Based on the data, we established correlations and provided the following reports: Customer Interaction with Support by Product + Sentiment Analysis Product Quality Deterioration Report Technical Support Team Efficiency Customer Loyalty: Assessment of support effectiveness from the perspective of repeat purchases. Identification of Common Phrases and Questions Results The research results, presented in reports along with our recommendations, enabled our client to: Optimize the Technical Support Department Improve the R&D Department's Efficiency Enhance Marketing and Content Department Performance Bundle Creation…
    uniE613 Optimization of Inhouse Data Flow – Migration to Amazon SP API and Airflow
    We helped our client optimize their data flow by migrating to Amazon SP API and Apache Airflow. The project involved integrating Amazon Vendor API, upgrading from Amazon MWS to SP API, and centralizing all data processing scripts in a robust cloud infrastructure. The result was improved data accuracy, enhanced automation, and a more scalable and flexible data management system, enabling the client to stay ahead in the competitive e-commerce landscape. Task As part of the update and optimization of the Inhouse Data Flow, our client faced the following challenges: Integration of Amazon Vendor API to obtain data for the first-party model (1P). Migration of the outdated Amazon MWS to the new version of Amazon SP API. Optimization of cloud infrastructure and consolidation of all data processing scripts in one place. Amazon Vendor To acquire data from Amazon SP API for Amazon Vendor, the following tasks were completed: Retrieval of Amazon Vendor Purchase Orders. Retrieval of Amazon Vendor Sales data. Retrieval of Amazon Vendor Inventory data. Amazon SP-API The following data retrievals were successfully completed: Amazon Orders/Items. Amazon FBA Fees. Amazon Shipments. Amazon Restock Inventory. Cloud Infrastructure Optimization As part of optimizing the cloud infrastructure on Amazon Web Services, all data processing scripts were migrated to Apache Airflow — a platform for orchestrating and managing data workflows. We utilize Airflow to execute scripts for data retrieval, transformation, and processing (ETL = Extract, Transform, Load) for our clients, ensuring reliability and effective monitoring of process execution. Result As a result of these optimizations and migrations, the client's data processing workflows became significantly more streamlined and efficient. The migration to Amazon SP API and Airflow led to: Improved data accuracy and consistency, thanks to the modernized data retrieval mechanisms. Enhanced process automation, reducing the need for manual interventions. Greater scalability and flexibility in handling increasing data volumes and complexities. A unified platform for managing data workflows, leading to better oversight and control over data processes. Overall, these improvements contributed to a more robust and agile data infrastructure, enabling the client to make more informed business decisions and maintain a competitive edge in the e-commerce sector.…

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