Data Engineer - Automation & Innovation Department

The role is with T-Mobile Polska within a diverse, international team of data professionals working across multiple countries. The team focuses on building a unified, high-performance data infrastructure and solving complex integration challenges to impact global data usage in the organization.

• Minimum 3 years of experience in a Data Engineering role.
• Practical experience with Cloud services, preferably GCP, including BigQuery, Dataflow, Google Cloud Storage (GCS), Data Catalog, and dbt.
• Advanced SQL proficiency including writing complex queries, query optimization, Common Table Expressions (CTEs), and window functions.
• Python coding skills; experience with Pandas and NumPy is a plus.
• Experience working with Apache Airflow for pipeline orchestration, automation, monitoring, data quality validation, alerting, and troubleshooting.
• Experience with Apache Spark, Hadoop, and Hive for large-scale data processing.
• Experience designing, developing, testing, maintaining, and optimizing ETL/ELT pipelines for batch and streaming data processing, including Apache Kafka and API-based data ingestion.
• Experience designing and implementing data integration solutions, data warehouse architectures, data models, and data layers for analytics and reporting.
• Experience implementing and maintaining CI/CD pipelines and using GitLab for version control and technical documentation.
• Experience working in Agile/Scrum environments.
• Good command of English.

Nice to have:

• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Economics, or a related field.
• Basic understanding of Machine Learning and MLOps principles.
• Strong understanding of data governance principles, including metadata management and data quality frameworks.
• Experience in international or multi-country data projects.
• Strong communication skills to translate technical findings into business-oriented insights.
• Self-starter with a continuous learning mindset and strong attention to detail.

• Build and maintain data ingestion processes from various sources into the Data Lake.
• Design, develop, and optimize complex data pipelines for reliable data flow.
• Build, develop, and maintain frameworks that facilitate the construction of data pipelines.
• Implement end-to-end testing frameworks for data pipelines.
• Collaborate with data analysts and scientists to ensure the delivery of quality data.
• Ensure robust data governance, security, and compliance practices.
• Explore and implement emerging technologies to improve data pipeline performance.
• Utilize and integrate data from various source system types, including Apache Kafka, MQ, SFTP, databases, APIs, and file shares.