jobs.pl/Пропозиції роботи/IT - Розробка програмного забезпечення/Senior Staff Data Platform Software Engineer (Java Distributed Systems & Kakfa)
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Senior Staff Data Platform Software Engineer (Java Distributed Systems & Kakfa)

Повна зайнятість Гібридна робота

Дата публікації: 19.09.2026

It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone—freeing people from busywork so they could focus on meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow— helping 85% of the Fortune 500® work smarter, faster, and better. We're building an AI-native culture where technology and talent are unstoppable together. And we're just getting started.

Join us to put AI to work for people.

Overview

The Data Platform group builds highly scalable, high‑performance platform capabilities for data‑in‑motion and backend storage systems. Our customers operate at massive scale, pushing the boundaries of data volume, throughput, and concurrency. We are looking for a seasoned IC5 Senior Staff Engineer with deep expertise in distributed systems and Data Lake architectures to drive next‑generation platform innovation.

Role Summary

As an IC5 Senior Staff Engineer, you will architect and deliver large‑scale distributed platform components, lead complex technical initiatives, and define engineering best practices. You will bring strong leadership, hands-on engineering depth, and the ability to design and operate reliable, scalable, and high‑performance data systems.

Experience Level

• 15–20 years of hands‑on engineering experience in distributed systems, data platforms, or large‑scale backend infrastructure.

Responsibilities

Architect, design, and build high‑performance distributed systems and platform components.; Build distributed systems data ingestion solutions with strong emphasis on scalability, quality, and operational excellence.; Deliver high‑quality, clean, modular, and reusable code while enforcing engineering best practices (code reviews, unit testing, automation, design reviews).; Build foundational libraries, frameworks, and tools focused on modularity, extensibility, configurability, and maintainability.; Collaborate across engineering teams to refine requirements and deliver end‑to‑end solutions.; Provide technical leadership for projects with significant complexity and risk.; Research, evaluate, and adopt new technologies that enhance platform capabilities.; Troubleshoot and diagnose complex production issues across distributed systems.

Requirements

Strong fundamentals in distributed systems architecture, design patterns, and algorithms.; Deep programming expertise in Java , including JVM internals, memory models, and garbage collection.; Proven experience in JVM performance tuning, profiling, and diagnosing performance bottlenecks.; Strong understanding of concurrency, networking, sockets, OS internals, and performance optimization.; Hands-on experience building and operating large‑scale distributed systems.; Experience with relational databases such as Oracle, MySQL, or PostgreSQL.; Experience with large‑scale deployments of distributed systems .; Deep knowledge of replication, fault-tolerant, and HA strategies.; Experience working in DevOps environments to operationalize distributed platforms.; Strong expertise in designing and architecting platforms using:; Kubernetes ( workload deployments, upgrades, monitoring and maintenance); Apache Iceberg (tables, catalogs, schema evolution, metadata management) is a plus; Apache Kafka (high‑scale clusters, topic/partition strategies, HA) is a plus; Apache Flink (stateful stream processing, exactly‑once semantics) is a plus; Apache Spark (batch & streaming jobs, optimization, partitioning) is a plus; Expertise in data formats such as Parquet, ORC, and Avro, along with compaction and governance strategies.; Ability to build scalable, fault‑tolerant ingestion and transformation workflows.; Experience integrating Data Lakes with analytics engines, query services, or ML platforms.
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