Senior Backend Data Engineer

• Compensation based on a B2B contract (via Devire)
• Rate: 140–170 PLN/hour net + VAT.
• 100% remote collaboration.
• Opportunity to access a benefits package in attractive prices (medical care, Multisport card, life insurance, cafeteria system).
• Long-term cooperation.

• Must-have Strong hands-on experience in backend engineering
• Very good knowledge of Java, used in data platform or data engineering contexts
• Practical experience with at least one JVM-based data processing framework (e.g. Flink, Beam, Dataflow, Spark)
• Solid understanding of SQL and experience working with cloud-based data analytics platforms, particularly BigQuery
• Practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery.
• Experience working with containerized applications and basic knowledge of Kubernetes
• Familiarity with cloud infrastructure as part of day-to-day DevOps work
• Experience working with production-grade data pipelines
• Ability to write high-quality, maintainable code and ship reliably to production
• Strong autonomy and ability to work effectively in an ambiguous environment without constant direction
• Should-have Experience with data engineering pipelines written in Scala and/or Python Previous exposure to platform-level engineering rather than only application-level development
• Experience collaborating within large, distributed engineering teams

Responsibilities

The project is part of a Data Infrastructure and Data Platform initiative focused on evolving and modernizing large-scale data processing systems. The contractor will be embedded in a Data Infrastructure team and will contribute directly to hands-on engineering work. Key responsibilities include: Implementing and supporting large-scale data pipeline migrations as part of company-wide platform evolution efforts Validating performance, reliability, and cost-efficiency of migrated workloads Contributing to the development and evolution of a modern data processing stack, including streaming and batch processing platforms Working with technologies such as Flink-based platforms and Lakehouse architectures Supporting production-grade data pipelines used across the organization Collaborating closely with platform and infrastructure engineers in a fast-paced, execution-focused environment This is a high-impact engagement with a strong focus on delivery, autonomy, and practical engineering outcomes.