{"id":1627,"date":"2026-03-26T10:00:00","date_gmt":"2026-03-26T09:00:00","guid":{"rendered":"https:\/\/olgamironczuk.pl\/?p=1627"},"modified":"2026-03-26T10:13:10","modified_gmt":"2026-03-26T09:13:10","slug":"moja-droga-rekrutacyjna-dashboard-analityczny-procesu-poszukiwania-pracy","status":"publish","type":"post","link":"https:\/\/olgamironczuk.pl\/en\/moja-droga-rekrutacyjna-dashboard-analityczny-procesu-poszukiwania-pracy\/","title":{"rendered":"My Recruitment Journey - An Analytical Dashboard of the Job Search Process"},"content":{"rendered":"
\n
\n
\"\"<\/figure>\n<\/div>\n\n\n\n
\n

Project:\u00a0<\/strong>My Recruitment Journey - An Analytical Dashboard of the Job Search Process
Tools:\u00a0<\/strong>Excel, Power Query, Looker Studio
Description:\u00a0<\/strong>An exploratory analysis of 18 applications for BI and data analytics positions. The project includes modeling the recruitment process, analyzing response times, assessing competency match levels, and examining the consistency between job seniority and recruitment outcomes. The dataset was frozen on the day the job offer was received.
Objective:<\/strong>\u00a0To structure the recruitment process as a measurable analytical model and identify factors potentially associated with progressing to subsequent stages of the recruitment process.
The project aimed to move from a subjective perception of the job market to an exploratory data analysis approach.
Data:<\/strong> Anonymized personal data related to job applications.
Repository:<\/strong> \"GitHub\" See on GitHub <\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n\n\n\n\n

\n\n\n\n

Introduction<\/h2>\n\n\n\n

The job search process was treated as a measurable and analyzable process rather than a series of individual applications. The aim of the project was to organize recruitment data and identify observed patterns in recruiter responses within the analyzed sample.<\/p>\n\n\n\n

The dashboard was built in Looker Studio as an interactive exploratory tool. The data modeling and transformation layer was developed in Excel, with Power Query used to create supporting tables.<\/p>\n\n\n\n

Data<\/h2>\n\n\n\n

The scope of the analysis covers 18 applications for positions related to Business Intelligence and data analytics, conducted in remote or hybrid work arrangements and based on full-time employment contracts.<\/p>\n\n\n\n

The dataset was frozen on the day the job offer was received. Any responses or recruitment decisions received after that date were not included in the analysis.<\/p>\n\n\n\n

The collected data included, among other things:<\/p>\n\n\n\n

    \n
  • the application submission date and the date of the first response,<\/li>\n\n\n\n
  • the final status of the recruitment process (as of the data freeze date),<\/li>\n\n\n\n
  • the job level specified in the job posting,<\/li>\n\n\n\n
  • the job level inferred from the stated requirements,<\/li>\n\n\n\n
  • the required technologies,<\/li>\n\n\n\n
  • the language of the job posting,<\/li>\n\n\n\n
  • the work arrangement and employment type,<\/li>\n\n\n\n
  • the percentage of requirements met,<\/li>\n\n\n\n
  • the recruiter response time.<\/li>\n<\/ul>\n\n\n\n

    The portfolio version has been fully anonymized.<\/p>\n\n\n\n

    Process<\/h2>\n\n\n\n

    1) Data modeling in Excel<\/strong><\/p>\n\n\n\n

    The main dataset was prepared in Excel, where:<\/p>\n\n\n\n

      \n
    • standardized technology names and job level categories,<\/li>\n\n\n\n
    • calculated the percentage of requirements met,<\/li>\n\n\n\n
    • computed recruiter response time,<\/li>\n\n\n\n
    • introduced a distinction between the declared job level and the inferred job level.<\/li>\n<\/ul>\n\n\n\n

      Due to the small sample size and the presence of outliers, the median was used as the main measure of response time.<\/p>\n\n\n\n

      2) Building supporting tables in Power Query<\/strong><\/p>\n\n\n\n

      In Power Query, two tables were created to support the analysis:<\/p>\n\n\n\n

      The technology table<\/strong>
      It enables the analysis of technology presence in job postings and comparison with recruiter response rates for postings that include a given technology.<\/p>\n\n\n\n

      The process stages table<\/strong>
      It supports the analysis of transitions between recruitment stages and the visualization of process flow.<\/p>\n\n\n\n

      Power Query was used for data transformation, aggregation, and the creation of auxiliary metrics, separating the data processing layer from the visual layer.<\/p>\n\n\n\n

      3) Methodology for inferring job level<\/strong><\/p>\n\n\n\n

      The job level was inferred based on the scope of responsibilities and requirements included in the job posting, taking into account:<\/p>\n\n\n\n

        \n
      • number of required years of experience,<\/li>\n\n\n\n
      • scope of responsibility (operational vs project-based),<\/li>\n\n\n\n
      • level of autonomy,<\/li>\n\n\n\n
      • complexity of tools and technologies,<\/li>\n\n\n\n
      • process or mentoring responsibility.<\/li>\n<\/ul>\n\n\n\n

        Based on this, positions were classified as junior or mid-level.<\/p>\n\n\n\n

        The aim was to examine whether consistency in job level (declared vs inferred) was observed in applications that received recruiter responses.<\/p>\n\n\n\n

        4) Visualization in Looker Studio<\/strong><\/p>\n\n\n\n

        The dashboard includes:<\/p>\n\n\n\n

          \n
        • KPI tiles (number of applications, response rate, rejection rate, median response time, average match level),<\/li>\n\n\n\n
        • distribution of final application statuses,<\/li>\n\n\n\n
        • analysis of median response time by status,<\/li>\n\n\n\n
        • visualization of transitions between stages,<\/li>\n\n\n\n
        • analysis of job level consistency,<\/li>\n\n\n\n
        • and a technology comparison table.<\/li>\n<\/ul>\n\n\n\n

          The color palette was limited to a single dominant color to ensure readability and visual consistency. <\/p>\n\n\n\n

          Results<\/h2>\n\n\n\n

          The analysis showed that:<\/p>\n\n\n\n

            \n
          • most applications did not progress to the next stage of the process at the time of data freezing,<\/li>\n\n\n\n
          • in the analyzed sample, a recruiter response occurred only once and happened very quickly,<\/li>\n\n\n\n
          • no response or rejection usually occurred after several weeks,<\/li>\n\n\n\n
          • the level of skill match was high across all groups,<\/li>\n\n\n\n
          • the only recruiter response concerned a job posting with a consistent job level, while inconsistent postings ended in rejection,<\/li>\n\n\n\n
          • recruiter responses were mainly related to the most frequently required market technologies (Excel, SQL, Power BI).<\/li>\n<\/ul>\n\n\n\n

            Due to the limited number of observations, the conclusions are exploratory in nature and apply only to the analyzed sample.<\/p>\n\n\n\n

            Reflections<\/h2>\n\n\n\n
              \n
            • The project demonstrates a process-oriented approach to data analysis, skills in data modeling and transformation using Power Query, and dashboard design as a tool supporting interpretation.<\/li>\n\n\n\n
            • The biggest challenge was maintaining a consistent methodology across multiple dimensions of the analysis and consciously separating observations from overinterpretation in a small sample.<\/li>\n<\/ul>\n\n\n\n
              <\/div>\n\n\n\n