Data Engineer

About the Role

IBM Quantum is building the world’s leading quantum computing systems, software, and cloud services. The Data Engineer will design and operate data pipelines that power insight into quantum hardware performance, system reliability, user workloads, and platform operations. This role involves collaboration with quantum hardware, firmware, cloud, and product teams to transform diverse technical datasets into trusted analytics assets for decision-making.

Your Role And Responsibilities

As a Data Engineer specializing in Data Integration, you will design and build solutions to transfer data from operational and external environments to the business intelligence environment, ensuring the seamless flow of data.

• Design Data Integration Solutions: Create and implement Extract, Transform, and Load (ETL) processes for data transfer.
• Develop ETL Processes: Build and maintain efficient ETL processes for accurate and timely data flow.
• Ensure Seamless Data Flow: Monitor and troubleshoot data integration issues, collaborating with stakeholders.
• Optimize Data Integration Solutions: Continuously evaluate and improve data integration solutions for process improvements and efficiency gains.

Key Responsibilities

• Design, build, and maintain scalable, reliable data pipelines supporting analytics, operational dashboards, and hardware performance insights.
• Contribute towards building IBM Quantum’s Lakehouse by implementing scalable data connectors.
• Develop and operate ETL/ELT workflows and tooling with a focus on data quality, accuracy, timeliness, and continuous improvement.
• Support large-scale batch processing for high-volume, heterogeneous datasets, including system telemetry, experiment metadata, cloud operations data, and device performance metrics.
• 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.

Requirements & Tech Stack

• Apply advanced SQL skills using PostgreSQL and Presto for 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).
• Work with streaming platforms such as Apache Kafka or IBM Event Streams to consume event-driven data.
• Apply streaming architecture concepts including topics, partitions, consumer groups, and schema evolution.

Qualifications

• Master's Degree
• 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.