(Senior) Analytics Engineer

<h3>Company Description</h3><p><strong>InPost Group is an innovative European out-of-home deliveries company, revolutionizing the way parcels are delivered to customers.</strong> With operations across several countries, our network of intelligent lockers (Paczkomat®) provides customers with a fast, convenient, and secure delivery option. Our mission is to provide best-in-class user experience for merchants and consumers. "Simplify everything" – redefining e-commerce logistics. We work by innovating the market with constant technological research and with meticulous attention to the customer.</p><p><strong>The Data &amp; AI department is seeking a (Senior) Analytics Engineer to join our Core Team.</strong> In this role, you'll shape analytical standards and implement innovative solutions, impacting our operations across Poland and 7 international markets. Remote work is possible.</p><p>Daily you’ll work with: Apache Spark in Databricks, Databricks various features, Python\PySpark, SQL, Kafka, Power BI, GitLab, Google BigQuery, inhouse data modeling tool.</p><h3>Job Description</h3><p><strong>On a daily basis you will:</strong></p><ul><li>drive innovation and improvements by evaluating new tools (e.g., Data Quality monitoring) and platform features (e.g., Genie Space on Databricks).</li><li>monitor the effectiveness of solutions by tracking implemented actions (e.g., naming convention adherence, metadata completeness, MR quality).</li><li>define workflows and coding standards for style, maintainability, and best practices on the analytical platform.</li><li>evangelize platform users on the best practices for its use and encouraging teams to continuously improve their working methods. Advocate for coding standards through various workshops and guidelines.</li><li>monitor the market for new tools and methodologies in data product development area.</li><li>while the role involves conceptual work, you'll also have opportunities for hands-on coding, such as analyzing AI readiness and implementing AI solutions to automate data development tasks.</li><li>work with various Data&amp;AI competencies (Data Consultants, Data Engineers, AI Engineers, Cloud Engineers, Data Architect)</li></ul><h3>Qualifications</h3><p><strong>Which skills should you bring to the pitch:</strong></p><ul><li>At least <strong>5 years of experience</strong> in an analytical role working with large datasets</li><li>Experience in <strong>data modeling and implementing complex data-driven solutions</strong> is a strong plus</li><li>Excellent proficiency in <strong>Python/PySpark</strong> for data analysis,<strong> SQL</strong> for data processing, bash scripting to manage Git repositories</li><li>Comprehensive <strong>understanding of the technical aspects of data warehousing</strong>, including dimensional data modeling and <strong>ETL/ELT processes</strong></li><li>Experience with <strong>real-time data processing</strong> and the ability to handle data from various backend/frontend systems. </li><li><strong>Familiarity with cloud-based data platforms</strong> (GCP/Azure/AWS)</li><li>The ability to present technical concepts and solutions to diverse audiences</li><li>Self-motivated with the ability to work independently and manage multiple tasks</li><li>Excellent interpersonal skills with the ability to collaborate effectively with cross-functional teams</li><li>Fluent in English: verbal and written</li></ul><p> </p><p><strong>Nice to have:</strong></p><ul><li>Experience in working with <strong>Apache Spark in Databricks</strong></li><li>Familiarity with modern data building tools like<strong> Apache Airflow, DBT</strong></li><li>Familiarity with data visualization tools such as PowerBI/Tableau/Looker</li><li>Knowledge of data governance principles and practices</li><li>Ability to thrive in a highly agile, intensely iterative environment</li><li>Positive and solution-oriented mindset</li></ul><h3>Additional Information</h3><p><strong>The course of the recruitment process:</strong></p><ul><li><strong>Step 1</strong>: HR Interview</li><li><strong>Step 2</strong>: Devskiller test</li><li><strong>Step 3</strong>: Technical Interview (60 min)</li><li><strong>Step 4</strong>: Home task</li><li><strong>Step 5</strong>: Home task presentation and discussion (60 min)</li></ul><ul></ul><ul></ul>