Head of Data Engineering

About Cypris
Cypris is the vertical AI platform for corporate R&D and innovation teams. We centralize scientific papers, patents, market news, and company intelligence into a single platform with over 500M+ global data points.

We're building AI Agents tailored to R&D workflows, powered by the latest models from OpenAI, Anthropic, and others. With Cypris, teams can manage projects, automate research workflows, and track markets, competitors, and technologies to accelerate product development and R&D strategy.

Today, leading mid-size to Fortune 100 companies in aerospace, genomics, cancer research, and autonomous systems rely on Cypris to innovate faster and with more clarity. Customers include the US Airforce, NASA, J&J, Yamaha and more.

About The Role
We are looking for a Head of Data Engineer to take full ownership of our data infrastructure — from ingesting raw, messy data to delivering reliable, queryable datasets that power innovation insights. You'll be responsible for designing, building, and monitoring pipelines that transform structured and unstructured inputs (APIs, SQL schemas, raw documents) into cohesive queryable relational and document based storage that is Gen AI ready.

This role is not just about writing pipelines — it's about owning the data layer end-to-end: ensuring scalability, cost-effectiveness, and reliability, while working transparently and collaboratively with the rest of the engineering team. You'll thrive here if you're motivated by startup urgency, love solving hard data problems, and want to shape the backbone of an AI-driven platform.

Responsibilities

• Design & implement pipelines to ingest and unify structured and unstructured data into a consistent, queryable system in an AI-ready fashion.
• Orchestrate and write workflows using Apache Airflow and Python for scalability and repeatability.
• Architect & optimize storage in relational and document based storage for both performance and cost efficiency. Propose new storage mechanisms as appropriate.
• Own production operations: monitor pipelines daily, troubleshoot proactively, and provide clear reporting on health and performance automating as much as possible.
• Work transparently: maintain a well-defined backlog in JIRA, post focused Pull Requests regularly, and communicate openly in team channels. Document and educate peers so others can help maintain and extend your work.
• Deliver with urgency: set and hit realistic milestones, maintain a strong say/do ratio, and ensure data never blocks business goals.

Requirements

• 5–8 years of experience in data engineering or closely related fields.
• Strong programming skills in Python.
• Hands-on experience with Google Cloud Platform (GCP) services for data engineering.
• Proficiency with Apache Airflow / Google Cloud Composer for workflow orchestration.
• Solid experience designing and optimizing data models in PostgreSQL and Elasticsearch.
• Familiarity with LangChain or similar frameworks.
• Proven ability to own production pipelines: from feature delivery to monitoring and reporting.
• Strong communication skills; experience working transparently in cross-functional teams.
• Thrives in a fully remote startup environment, with the resilience and urgency to deliver under pressure and the desire to help build the future.

Nice to Have

• Experience with Java / Spring Boot or Angular, to collaborate more closely with full-stack engineers.
• Background in building pipelines that support GenAI or LLM applications through experience with frameworks like LangChain or similar in-house efforts.

Why Join Us

• Build the backbone of a platform at the intersection of data and AI.
• Tackle complex challenges in data integration, scalability, and cost optimization.
• Work transparently and collaboratively in a team where ownership is valued.
• Be part of an early-stage startup where speed, impact, and growth with a positive attitude are the norm — not the exception.