Position: Data Analyst/Data Engineer
Category: Data Science
English level: fluent
Years of experience: 2.5

Automated reports and data-check process, which can be run from Terminal, which helps avoid mistakes of manual coding and overlooking important data deviations, while also saving a lot of time.
-- I've worked as a Data Analyst for a telecom company for almost a year. My day-to-day tasks included designing and delivering regular and ad-hoc reports. What I mostly enjoyed was reporting-process automation (SQL procedures and jobs, Power BI dashboards (some DAX), integrated with RDBMS and SQL time-intelligence functions (dateadd(),add_months() etc.). 
-- My next employment was with a web-analytics company where I provided a wide variety of standardized and custom marketing reports, while also ensuring the quality of the delivered data. I mostly used such tools as SQL (analytic/window functions), Python (pandas, NumPY), AWS (Athena, databricks, S3), Excel. While working there I got some experience with Terminal (cmd), Jira, GitLab and Tableau, worked with large datasets.
-- I currently work as a Product Analyst with a company, which offers tools for personal digital security and computer performance enhancement. I develop regular and ad hoc Tableau reports (Tableau Desktop + Tableau Online), derive insights from AB tests and other data-driven investigations in order to improve funnel, user journey and drive revenue and retention. One of my projects is about collecting customer feedback results (in-house surveys + web scaring) and performing Sentiment Analysis (Python).
I've also done various data-preparation process automations, such as LTV predictions automation (Python). In terms of data preparation I've also worked with Google API, cron jobs, used some AWK (logs analysis).
One of my work pet projects is related to introducing version management process within BI team (GitLab).
I've realised that Data Engineering is my area of interest and I'm highly motivated to switch to this position.
Using English on a daily basis would be a great plus.
I highly value friendly working environment. It doesn't matter if the project is big or small, but I want to be able to work with large datasets, Python, preferably cloud technologies (AWS Services) and to be using English on a daily basis. I'm not interested in Product Analyst positions as I want to switch to Data Engineer position and focus on data pipelines rather than on product-related insights. However, I don't mind if some of my work routine includes deriving insights from the data, especially at the beginning.