Senior Data Engineer (Big Data)
Summary
Build and maintain scalable batch and streaming data pipelines using PySpark, Kafka, and cloud-native tools to process high-volume event data for a large-scale data platform.
Contracting period:Long term project(1 year)
Location: Hybrid (Poland)
About the Role
We are looking for an experienced Senior (Big) Data Engineer to join large-scale data platform initiatives for an international technology-driven organization. The company builds and operates high-volume, low-latency data platforms, processing large amounts of event data through modern batch and streaming architectures.
This role is suited for a senior-level data engineer who enjoys working with distributed systems, event-driven data processing, and cloud-native technologies. The position requires fluency in Polish and location within Poland.
Your Profile
6–8 years of hands-on experience in Data Engineering roles
Experience with at least one major cloud platform (GCP, AWS, or Azure); willingness to work in a GCP-based environment (prior GCP experience is a plus)
Strong production experience with Apache Spark, using Python / PySpark
Hands-on experience with streaming and event-driven architectures, using technologies such as:
- Kafka
- Google Pub/Sub
- AWS Kinesis
Strong SQL skills, including data transformations, analytical queries, and performance optimization
Nice-to-Have Skills
- Previous experience specifically in a Big Data Engineer role
- Background in JVM-based languages (Scala, Java, Kotlin)
- Familiarity with data lake or lakehouse architectures
- Experience implementing monitoring, observability, and data quality checks
- Exposure to high-throughput event processing systems
- Experience with CI/CD pipelines or Infrastructure-as-Code approaches
Your Responsibilities
Design, build, and maintain scalable batch and streaming data pipelines
Develop, optimize, and operate Apache Spark jobs using PySpark
Work with event-driven and streaming platforms to process high-volume datasets
Perform advanced data transformations and analytics using SQL
Improve the performance, reliability, and observability of data pipelines
Collaborate with analytics, platform, and product teams to deliver end-to-end data solutions
Participate in technical and architectural decision-making
Take end-to-end ownership of data solutions, from design through production
What You Can Expect
Work on large-scale, data-intensive systems with real-world impact
A technically challenging environment focused on distributed data processing
Collaboration with cross-functional teams in a modern data platform ecosystem
Opportunities to influence architecture, tooling, and best practices