Data Engineer

Data Engineer

At OQC, we aren’t just theorising about the future; we’re building it. Born from a philosophy of bold innovation, we’ve successfully transitioned quantum computing from an academic dream into a commercial reality. The most exciting thing is that we’re just getting started and we’ve recently closed our £260 million Series C funding round – the largest fundraise ever completed by a quantum computing company in Europe.

The Purpose

As a Data Engineer, you'll build the governed data foundations behind OQC's machine learning and quantum workloads. You'll turn sensitive customer data into secure, traceable and high-quality datasets that support training, evaluation and synthetic-data use cases across cloud, on-premise and datacentre environments.

The Role

Based in London and working closely with our Platform Engineering team, you'll own data pipelines from ingestion through transformation, governance and consumption. You'll work with technologies including Databricks, Spark and Delta Lake to make complex data reliable, secure and ready for use across OQC's customer-facing applications.

You'll collaborate across Software, Platform, Security, Machine Learning and Infrastructure teams, solving the challenges that come with moving sensitive data between different environments while maintaining quality, lineage, access controls and traceability.

What You'll Be Working On

• Design, build and operate secure data ingestion and transformation pipelines across internal, external and customer data sources.
• Build data workflows spanning cloud, private-cloud, on-premise and datacentre environments.
• Develop governed datasets for machine learning and quantum workloads, defining data contracts, versions, quality rules, entitlements, lineage and retention.
• Create robust training, validation, calibration and untouched test datasets that enable credible ML evaluation.
• Transform raw customer data into QPU-permitted features or latent representations and return outputs into governed synthetic datasets.
• Monitor and optimise pipeline reliability, throughput, latency, storage utilisation and operational cost.
• Build automated testing and CI/CD practices for data pipelines and infrastructure, partnering across engineering teams to deliver reliable data foundations for customer-facing products.

What We're Looking For

• Strong hands-on experience with Databricks, Spark, Delta Lake and SQL.
• Experience building and operating streaming and batch data pipelines.
• Strong understanding of data quality, lineage, access control and dataset governance.
• Experience operating data systems across cloud infrastructure and/or private or on-premise environments.
• Understanding of the specific data requirements associated with ML training, synthetic data and untouched evaluation datasets.
• Strong understanding of data security, including encryption in transit and at rest, access controls and audit logging.
• Strong ownership and communication skills, with the ability to collaborate effectively across software, platform, security, ML and infrastructure disciplines.
• Degree or equivalent practical experience in Computer Science, Software Engineering or a related discipline.

The 'Nice-to-Haves'

• Experience working with sensitive or regulated datasets, particularly banking transactions, market data or customer records.
• Experience supporting data infrastructure used by machine learning workloads.
• Familiarity with quantum computing or hybrid classical-quantum products.
• Experience working with data platforms that span cloud and customer-controlled infrastructure.
• A pragmatic, curious approach and comfort working in a fast-evolving technical environment.
• A relevant professional or postgraduate qualification.

Why Join OQC

You will join a world-class team at the forefront of the next computational era. We offer a culture of bold innovation, the chance to work with unique lab infrastructure, and the opportunity to see your work redefine the limits of computation.

Learn more about our benefits and positive work culture here: