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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.

Required education

Bachelor's Degree

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.

• Integrate multiple technical data sources—quantum hardware telemetry, calibration data, experiment logs, job execution data, user activity, system health metrics—into trusted analytical datasets.

• Collaborate with quantum hardware, software, product, SRE, and analytics teams to translate requirements into robust, production-ready data solutions.

• Use Git-based version control, contribute via code reviews, and follow industry-standard software engineering best practices.

Preferred technical and professional experience

· Experience with Lakehouse solutions and architectures, including IBM watsonx.data

· Experience with distributed analytics engines such as Presto/Trino, or Apache Spark

• Familiarity with data modeling techniques for analytical and reliability engineering use cases.

• Exposure to data governance concepts such as access control, dataset ownership, lineage, and lifecycle management.

• Experience operating data pipelines in cloud-based or distributed environments (e.g., hybrid cloud, containerized systems).

• Experience working with hardware telemetry, infrastructure monitoring data, or high‑volume operational datasets.

• Interest in or exposure to quantum computing, advanced hardware systems, cryogenics, or other deep‑technology platforms.