Data Engineer

CyberCube; United States; Remote; USD 150,000 - 210,000; Full time

Benefits offered by this job

Learning and development assistance

Vision and dental coverage

Extra vacation days during winter

Flexible working hours

Work-from-home option

Mentorship programs

Secondment programs

Work abroad up to 3 months with visa

AI Fluency at CyberCube

Job summary

CyberCube Analytics, Inc. is hiring a Senior Data Platform Engineer to own the architecture of the data layer feeding dashboards, apps, and AI agents across the business. You will design scalable, governed data access that is auditable and secure, enabling rapid analysis of sensitive portfolio data.

Our stack includes Python, GraphQL/Hasura, MySQL, Redis, Elasticsearch, Spark, Iceberg, Airflow, Redshift and AWS. You will shape data models, pipelines and AI interfaces across teams.

Qualifications

• Experience in data/backend engineering with strong Python and production API experience.
• Hands-on GraphQL design experience (Hasura a bonus).
• Relational DB skills on MySQL/PostgreSQL; schema design and indexing.
• Experience with Airflow and lakehouse tools (Spark, Iceberg, Redshift, Athena).
• Track record of authoring/reviewing system design docs.
• Real AI experience in engineering/data projects and daily AI tooling use.
• Knowledge of MCP servers or RAG pipelines is a plus.
• AI observability tooling (tokens, latency, failure modes).
• LangGraph/Claude Agent SDK/OpenAI Agents SDK familiarity desired.
• Guardrails and prompt-injection defense knowledge.
• Elasticsearch/OpenSearch experience.

Responsibilities

• Data as a product: set up contracts, versioning, docs, SLAs, and self-service onboarding for data access.
• Modelling & migration: design schemas for transactional/analytical access and migrate datasets.
• AI-facing interfaces: build tool definitions, scoped permissions, metadata exposure and audit trails.
• Agent vs. pipeline judgment: decide use-cases for automation vs deterministic pipelines and implement tooling.
• Platform health: improve scalability, reliability, and cost across Spark, Iceberg, Airflow and AWS.
• Governance: own data lineage, quality checks, and isolation for client-sensitive data.
• People & direction: write/review design docs and translate requirements into epics.

Skills

Data backend engineering

Python programming

GraphQL design

AI in engineering

System design documentation

API development

AI observability

RAG pipelines

LangGraph/Claude Agent SDK

AI tooling

ElasticSearch skills

Tools

Python

GraphQL (Hasura)

MySQL

PostgreSQL

Redis

Elasticsearch/OpenSearch

Spark

Iceberg

Airflow

Redshift

AWS

Job description

Описание: 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.

Задачи:

• 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

Real ownership. You set the technical direction across several teams, not just one backlog. AI work that ships. You'll build MCP servers, semantic layers and eval harnesses for production, not demos. Work that matters. Our analytics help the insurance industry understand and price cyber risk. Engineers who use the tools. Claude Code, Cursor and agentic workflows are part of how we work every day. Play an instrumental role in reshaping one of the oldest industries in the world

Learning and development assistance Vision and dental coverage Extra vacation days during winter holidays Flexible working hours and work-from-home option Company-supported mentorship and secondment programs Work abroad for up to 3 months with approval AI Fluency at CyberCube AI is reshaping how work gets done across every function.

We value people who are curious about AI, eager to learn, and thoughtful about applying AI tools to work more effectively. AI fluency is part of how we assess every role in our hiring process.

Требования:

• Experience in data or backend engineering, with strong Python and production API experience
• Hands-on GraphQL design (Hasura is a bonus)
• Deep relational database skills on RDS MySQL/PostgreSQL or similar: schema design, indexing, tuning
• Experience with Airflow plus lakehouse tools (Spark, Iceberg, Redshift, Athena)
• A track record of authoring and reviewing system design documents
• Real experience applying AI to engineering or data work, and daily use of AI coding tools
• MCP servers or RAG pipelines
• Agent eval harnesses AI observability (tokens, latency, failure modes)
• LangGraph / Claude Agent SDK / OpenAI Agents SDK or your own orchestration
• Guardrails and prompt-injection defence
• Elasticsearch/OpenSearch

CyberCube Analytics, Inc. and CyberCube Analytics Europe Limited is an equal opportunity employer. We don’t tolerate discrimination against age, gender, gender identity, gender expression, sexual orientation, race, color, nationality, ethnicity, religion, disability, veteran status, protected genetic information or political affiliation.