Python Data Engineer

XTB is a global company from the financial industry, focusing on online trading of financial instruments. It is the largest FinTech in Poland and a leader in Central and Eastern Europe, operating in several countries including Asia and South America. The company offers training and development programs and supports employee growth in an international business environment.

• 3–5 years of professional experience in data engineering or a closely related software engineering role.
• Strong data warehousing knowledge: dimensional modeling, normalization vs. denormalization, ETL/ELT design, SCDs, and warehouse performance considerations.
• Hands-on experience with Apache Spark using PySpark, including building and optimizing distributed data processing jobs.
• Strong general Python development skills with a solid programming and computer-science foundation (data structures, algorithms, OOP, testing).
• Proficiency in SQL, including complex queries, window functions, and query optimization.
• Experience with version control (GitLab).
• Understanding of data quality, reliability, and pipeline monitoring practices.

Nice to have:

• Experience with cloud data platforms (AWS, Azure, or GCP) and their data services.
• Familiarity with modern data platforms or lakehouse technologies.
• Experience with CI/CD for data pipelines and infrastructure-as-code.
• Exposure to streaming data processing (e.g., Spark Structured Streaming, Kafka).
• Experience working in Agile teams.

• Design, build, and maintain batch and incremental data pipelines using Spark (PySpark) on large datasets.
• Model and develop data warehouse layers (staging, dimensional, and serving) using sound DWH principles such as star/snowflake schemas, fact and dimension tables, and slowly changing dimensions (SCDs).
• Develop and optimize ETL/ELT workflows, ensuring data quality, accuracy, and timeliness.
• Write clean, well-tested, and maintainable Python code, applying solid software engineering practices (version control, code review, modular design).
• Tune Spark jobs for performance and cost - partitioning, caching, join strategies, and resource configuration.
• Build and maintain orchestration workflows and monitor pipeline health.
• Collaborate with stakeholders to translate business and analytics requirements into reliable data models.
• Contribute to data architecture decisions, documentation, and engineering standards within the team.
• Mentor junior engineers through code reviews and knowledge sharing.