Data Engineer

Introduction

At IBM Research, we are the innovation engine of IBM. Exploring what’s next in computing and shaping the technologies the world will rely on tomorrow. From advancing AI and hybrid cloud to pioneering practical quantum computing, we anticipate challenges and unlock new opportunities for clients, partners, and society. Working in Research means joining a team that accelerates discovery at the intersection of high-performance computing, AI, quantum, and cloud. You’ll collaborate with leading scientists, engineers, and visionaries to push boundaries and turn ideas into reality. With a culture built on curiosity, creativity, and collaboration, IBM Research offers the opportunity to grow your career while contributing to breakthroughs that transform industries and change the world.

Your Role And Responsibilities

IBM Quantum is building the world’s leading quantum computing systems, software, and cloud services. The Data Engineer in this role will design and operate the data pipelines that power insight into quantum hardware performance, system reliability, user workloads, and platform operations. You will work closely with quantum hardware, firmware, cloud, and product teams to turn diverse technical datasets into trusted analytics assets that guide decision‑making across IBM Quantum’s roadmap.

Preferred Education

Master's Degree

Required Technical And Professional Expertise

• Design, build, and maintain scalable, reliable data pipelines supporting analytics, operational dashboards, and hardware performance insights for IBM Quantum systems.
• Develop and operate ETL/ELT workflows with a focus on data quality, accuracy, timeliness, and continuous improvement.
• Apply advanced SQL skills using PostgreSQL and Presto to support analytical workloads, including complex queries and performance tuning.
• Build and operate orchestration workflows in Apache Airflow, including dependency management, retries, backfills, monitoring, and operational reliability.
• Implement data transformations and validations using Python (e.g., pandas and related libraries).
• Support large‑scale batch processing for high‑volume, heterogeneous datasets, including system telemetry, experiment metadata, cloud operations data, and device performance metrics.
• Work with streaming platforms such as Apache Kafka or IBM Event Streams to consume event‑driven data from distributed quantum systems and services.
• Apply streaming architecture concepts including topics, partitions, consumer groups, and schema evolution.