Data Engineer (maternity cover)

Delivery Experience is one of the fastest-growing areas of Allegro where we undertake new, complex projects to enhance logistics and warehousing processes. We are building technology that makes Allegro's deliveries easy, cost-effective, fast and predictable. Our team takes care of critical services along the Allegro shopping journey, responsible for predicting delivery times using statistical algorithms and machine learning, selecting the best delivery methods tailored to customers, and integrating with carrier companies. As a contributor in this position, you will help us grow our cutting-edge cloud analytical platforms, establishing the groundwork for the future of logistics intelligence.

• At least 3 years of experience as a Data Engineer working with large datasets.
• Expert proficiency in SQL.
• Strong understanding of data modeling and cloud Data Warehouses (DWH).
• Hands-on experience with cloud warehouses like BigQuery or Snowflake (BigQuery preferred).
• Experience in designing and maintaining ETL/ELT processes.
• Experience working with large datasets in Python, utilizing data processing tools like PySpark.
• Familiarity with data orchestration tools like Airflow.
• High degree of autonomy and passion for translating complex business requirements into impactful data solutions.
• Familiarity with and adherence to good software engineering practices (clean code, code review, CI/CD).
• Proven capability to optimize cost and efficiency of data processing.
• Demonstrated ability to leverage AI/ML tooling to enhance data pipelines and analytical products.
• English proficiency at least B2 level.

• Develop and maintain data ingestion and processing pipelines for large volumes of data using BigQuery, Airflow, and dbt.
• Design and streamline data architecture that powers analytical products.
• Help drive the adoption of AI in analytics and unlock new possibilities on top of our data mesh.
• Develop tooling for monitoring and enhancing data quality and integrity.
• Manage and optimize costs related to data processing and infrastructure.