Hi there, it is Dmitriy. I am a data analyst specializing in digital commerce analytics. In my professional life, I help stakeholders and teams make informed decisions regarding marketing strategies or developing and promoting products. In start of career, I was responsible for ensuring accurate marketing data collection and analysis. Now, in Senior level, I can take a broader approach, supporting the entire insights delivery process into any digital commerce problem.
I live in Netherlands, Amsterdam. it's an amazing country with great culture and lifestyle. I moved from Russia in 2022 due to circumstances beyond my control. Traveling and learning local culture has been my most valuable hobby lately. However, I also follow popular science topics on YouTube and play chess some time.
Transforming Business Needs into Measurable KPIs
On the agency-side, I had the opportunity to work with a diverse range of companies, teams, and stakeholders. Each company aimed to promote goods and services with maximum effectiveness and make their customers more happy. To ensure together we can achieve their goals, I leverage my curiosity to ask insightful questions about business processes, product features, cutomer needs. By translating the answers into measurable metrics like CR, CPA, ROI, channel traffic distribution and amount, I enable us to track the success of our digital campaigns and demonstrate their impact on business objectives. As example I developed set of a KPI for e-commerce project by reselling sportswear based on metrics like ROAS, Click Cost and Attributed Orders Cost for each group of goods. That eventually led to increase in business total revenue more than 15%.
Data warehousing and accuracy monitoring
When I was working for the Bank, the primary technical objective was to establish a system for collecting and storing all necessary online user information to ensure data integrity. I developed a cloud-based solution utilizing Google Cloud Platform (GCP), specifically BigQuery as the database, Cloud Storage for API file storage, and Dataflow as the data pipeline manager. To guide the development process, I employed Google's Digital Maturity Framework. As the project progressed, I acquired proficiency in various data engineering skills such Apps Script, Node JS, data pipeline construction technique and data processing best practice. As a result, the Maturity Benchmark score increased from 1.2 to 4.6, indicating the availability of all required data for analysis and conclusion-drawing.
Perform analysis and draw insightful conclusion
While working for a financial trading company, the most challenging aspect was assessing the performance of the marketing mix across diverse markets in Asia, Africa, and Eastern Europe. My role involved generating weekly reports on online marketing effectiveness for SEO, Paid, Social, and Developer teams, as well as the Marketing and Sales VP.
From scratch, I developed a website tagging map schema and created weekly performance reports using Jupyter Notebooks. I employed strong storytelling skills to visualize the effectiveness of each channel across different countries and products, using appropriate charts and narratives. Numerous reports contains statistical indicators to assess the significance of our weekly results, making them understandable for non-technical colleagues and identifying potential random results. These reports empowered the VP of Marketing and Sales to make informed decisions about expanding into new markets.
Empliment data-driven approach in organisation
The past two years was particularly challenging, demanding both increased focus on SEO (search engine optimization) and conversion rates CR. This dual challenge opened something new in my understanding of significant role of data driven aproach in decision making. Through collaboration between three teams – developers, product managers, and marketing – I can build a confluens based system that generates data-driven hypotheses about audience behavior, product features, landing pages increasing organic traffic. This system would enable coworkers to continuously obtain critical information about each issue in the most efficient way possible. This could be achieved through dashboards, ad-hoc reports, messenger integrations, and task tracker integrations tied in single point - confluence documentation.
Eventually this turns out we've reached almost each our annual goals with AAA rate
Make segnificat contribution to product development process
I collaborated with product owners to develop a system of product metrics such as new signups, feature interaction clicks, promoted impressions, DAU, MAU. I crafted product perfomance dashboard allowed my colleague to measure how efficiently user interacts with product. This dashboard played crucial role for generating hypnotizes to improve product attributes and it's feature set. As a result new features hypnotizes started show significant result in A/B tests.
My personality type according https://www.16personalities.com/
Advocate is a personality type with the Introverted, Intuitive, Feeling, and Judging traits. They tend to approach life with deep thoughtfulness and imagination. Their inner vision, personal values, and a quiet, principled version of humanism guide them in all things.
Good at thinking things through: Advocates are naturally like to analyze and understand things deeply, which is perfect for data analysis jobs where you spend a lot of time looking closely at information.
Can see things from other people's shoes: They're good at understanding what others are thinking and feeling. This can be helpful in data analysis because it can help them figure out what customers need and want based on the data.
Careful and detail-oriented: Advocates like to do things right and make sure everything is accurate. This is important in data analysis because mistakes can lead to wrong conclusions.
Goal-driven: Advocates want to make a positive impact. In data analysis, this means using data to solve problems, improve how things are done, or find important information to help make decisions.