Senior Data Engineer (Big Data)
Data Edge · Varsòvia, Warsaw, Poland ·
- Seniority
- Senior
- Category
- Data engineering
- Experience
- 1+ years
Data Edge · Varsòvia, Warsaw, Poland ·
prox-works · India
GFT Technologies
Sowelo Consulting sp. z o.o.
emagine · Warsaw, Mazovia, Poland
Location: Hybrid (Poland)
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.
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
Azure Event Hubs
Strong SQL skills, including data transformations, analytical queries, and performance optimization
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
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
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