Senior Data Engineer

Are you passionate about safeguarding countries, societies, and businesses from hybrid threats?

Look no further! Our rapidly scaling company is committed to this crucial mission, collaborating with some of the world’s most influential organizations, including NATO and the EU.

If you’re ready to join a high-impact team shaping the future of intelligence and security, we invite you to become part of our journey. Join us and help defend the digital world with purpose and innovation!

Role Overview
We’re looking for a Senior Data Engineer to own the data foundation behind our solutions: how data is processed, stored, linked and served. Every month we process billions of data points of different types (text, images, social media, geospatial and time-series data). Once data is collected by our data fetching services, your job is to make sure it’s processed reliably, connected across sources and storage systems, and fast to query, so that analysts and AI agents get what they need in seconds.

You’ll work closely with our AI Engineers, who own the entity model and the knowledge layer on top of the data, and you’ll give them a fast and solid data foundation to build on.

This is a hands-on senior role with real ownership: you’ll choose the tools, run experiments and own the results end to end, from pipelines and infrastructure to the features our users see.

Key Responsibilities
Multimodal data and storage
Build and own the pipelines that take data from our collection services, then process it, enrich it and load it into our data storage at scale.
Decide how each type of data (text, images, geospatial, time-series) is stored and indexed across relational, columnar, search and other storage, so that it can be queried efficiently together.
Link data across storage systems so that everything related to the same object can be retrieved quickly and consistently.
Provide fast, reliable data access for AI engineers, analysts and product services.
Work with product, data fetching and AI teams to deliver data-driven features to end users, such as new data sources in the product, faster search, alerts and reports.
Core data platform
Migrate production workloads from PostgreSQL to StarRocks and redesign storage for billion-row scale.
Improve the performance, reliability and cost of data processing.
Own the deployment and infrastructure of the data platform.

Requirements
5+ years in data engineering (or backend engineering with a strong data focus), working on production systems that process large data volumes.
Hands-on experience with several types of storage (e.g. relational, columnar/OLAP, search engines, document stores, object storage) and the judgment to pick the right one for each task.
Experience working with different data modalities. Text is a must; images and/or geospatial data are a strong plus.
Experience building large-scale pipelines that are resumable, idempotent and memory-safe.
A track record of building data platforms and custom solutions yourself, not only working within managed platforms (e.g. Databricks).
Experience delivering data that powers user-facing product features.
Ability to read query plans and restructure physical data layout for performance.
Production experience with Python backend services and APIs.
Comfort owning infrastructure (Docker, Kubernetes, CI/CD, infrastructure as code).
Active use of AI tools in your daily work.
Upper-intermediate level of English or higher.

Nice to Have
Understanding of knowledge graphs, ontologies, entity extraction, linking or resolution.
Graph databases or graph modelling on relational or columnar storage.
GIS tooling (e.g. PostGIS).
StarRocks, ClickHouse or other MPP engines; Elasticsearch or OpenSearch at scale.
Kafka or RabbitMQ, Airflow, dbt.
Long-running dual-write migrations, PostgreSQL internals, observability.
A background in OSINT, threat intelligence or defence.

We Offer
Opportunity to contribute to a mission-driven startup and support prestigious clients, including governments worldwide, enterprise clients, and leading NGOs in tackling global security challenges.
Autonomy and freedom to drive experiments and bring your own ideas to life.
Flexibility of fully remote work.
Flexible, unlimited time-off policy.