Data Engineer - Scalable Data Pipelines & Apps (Equity)

CuspAI; Greater London; On-site; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Equity in CuspAI

25 days holiday UK

Professional development budget

Job summary

CuspAI in London is seeking a Data Engineer to own, design, develop, test, secure, and deploy data-rich tools, services, and web applications. You will build foundational data systems enabling materials scientists, ML engineers, and partners to collaborate on diverse datasets at scale.

You will bridge complex materials science data with real-world applications, empowering researchers and models with clean, structured data to accelerate discovery and design of breakthrough materials.

Qualifications

• Strong software engineering background focused on data management.
• Deep fluency in Python.
• Experience with multiple database paradigms (Relational, Document, Columnar).
• Proven track record building data-rich Python APIs and web UIs.
• Experience with unstructured data and large datasets with privacy standards.
• Experience with orchestration frameworks (Dagster, Flyte, Prefect, Ray).
• Basic infrastructure skills with Docker and Kubernetes.

Responsibilities

• Own end-to-end data tools and web applications from design to deployment and security.
• Build scalable data ingestion, processing, and cleaning pipelines for structured and unstructured data.
• Develop data APIs and interfaces for querying, exporting, analyzing, and visualising data.
• Implement orchestration to automate pipelines and manage data flows.
• Maintain data privacy, governance and security across architectures.
• Deploy and manage containerised data services with basic infra tooling.

Skills

Python

Data management

Relational & NoSQL DBs

Data privacy

Orchestration frameworks

APIs & UI

Tools

Docker

Kubernetes

Job description

CuspAI in London is seeking a Data Engineer to own, design, develop, test, secure, and deploy data-rich tools, services, and web applications. You will build foundational data systems enabling materials scientists, ML engineers, and partners to collaborate on diverse datasets at scale.

You will bridge complex materials science data with real-world applications, empowering researchers and models with clean, structured data to accelerate discovery and design of breakthrough materials.