Senior Data Engineer

About the role

About CyberCube: https://www.cybcube.com/careers

CyberCube delivers the world's leading analytics to quantify digital risk, helping the largest global carriers, reinsurers and brokers understand, price and manage cyber risk in financial terms.

- AI at our foundation, cyber risk at our core. We don't just use AI, we shape it.

- Built on AI from day one. Artificial intelligence has been part of our strategy since the beginning, blended with deep cybersecurity and insurance expertise and backed by rigorous testing.

- Trusted by more than 100 clients, including 75% of the top 40 European and US cyber insurance carriers and 70% of the top ten reinsurance brokers.

- Backed for global growth. In 2025 Spectrum Equity joined some of CyberCube’s financial partners as a new cornerstone investor, accelerating our global growth and fueling innovation across our end-to-end cyber risk analytics for the insurance industry.

- A truly global team across San Francisco, New York, London, and Tallinn.

- A culture of collaboration, openness, intellectual rigor, and ownership for excellence.

- People at the forefront. We encourage CyberCubers to challenge themselves, push boundaries, and do the best work of their careers.

Most data platforms were built to feed dashboards. Ours has to feed dashboards, applications, and the AI agents we're building to let customers and our own teams dig into sensitive portfolio data far deeper than a dashboard allows — fast, governed, and auditable to the row.

We're hiring a Senior Data Platform Engineer to own the architecture of that layer. Everyone's talking about "AI-ready data infrastructure." This is the job where you actually build it.

What you'll own

- Data as a product: You'll set up contracts, versioning, docs, SLAs and self-service onboarding, so product teams can use the data access layer without asking us every time.

- Modelling & migration: You'll design schemas for both transactional and analytical access, and lead the move of existing datasets onto them.

- AI-facing interfaces: You'll build tool definitions, scoped permissions, metadata exposure and audit trails, so agents can query data safely.

- Agent vs. pipeline judgment: You'll decide where agentic automation helps and where a deterministic pipeline is the better choice, then build the tooling to match.

- Platform health: You'll improve scalability, reliability and cost across Spark, Iceberg, Airflow and AWS.

- Governance: You'll own lineage, quality checks and isolation for client-sensitive data, whether a person or an agent is reading it.

- People & direction: You'll write and review design docs and turn requirements into epics

Our stack

Python · GraphQL/Hasura · MySQL · Redis · Elasticsearch · Spark · Iceberg · Airflow · Redshift · AWS (S3, Glue, Lambda, RDS, Containers)

Why to join this team