Data Engineer

About the Role

As a Data Engineer at Futuria, youll play a central role in designing, building, and maintaining the scalable data infrastructure that powers our AI models and intelligent applications. Youll collaborate with AI engineers, data scientists, and product teams to ensure the seamless flow of high-quality, reliable data that drives performance and insight across the platform.

Key Responsibilities

• Design and implement scalable, secure, and reliable data pipelines to support procedural workflow orchestration and AI agent workflows
• Develop and manage robust data ingestion processes (batch and streaming) from diverse sources
• Collaborate with AI engineers and product teams to define data requirements and integrate pipelines seamlessly with models and applications.
• Build and maintain ETL/ELT processes that ensure data integrity, consistency, and accuracy across systems.
• Optimize data infrastructure for performance, cost efficiency, and scalability in cloud environments.
• Develop and manage graph-based data systems (e.g. Kuzu, Neo4j, Apache AGE) to model and query complex relationships in support of Retrieval Augmented Generation (RAG) and agentic architectures.
• Contribute to text retrieval pipelines involving vector embeddings and knowledge graphs, for RAG.
• Deploy and maintain vector databases (e.g. Chroma DB, Milvus) and prompt caching systems (e.g. via Redis)
• Monitor, test, and troubleshoot data flows to maintain real-time access to critical information and ensure system reliability.

About You

We are looking for a self-starting Data Engineer who thrives in a fast-moving startup environment, is comfortable experimenting and iterating quickly, and is excited to work at the frontier of AI-driven products and agentic systems.

Essential:

• Strong proficiency in Python and SQL for data engineering tasks
• NoSQL experience (e.g. MongoDB, Cassandra)
• Experience with message queues and publish-subscribe (e.g. NATS, RabbitMQ etc.)
• Proven experience designing, building, and maintaining data pipelines.
• Containerization experience (e.g. Docker)
• Hands-on experience with vector databases.
• Experience integrating language models (LMs) into data workflows.
• Proficiency with cloud platforms such as Azure, AWS, or GCP and their managed data services.

Desirable:

• Experience with asynchronous python programming
• Experience with graph technologies (e.g., Kuzu, Neo4j, Apache AGE).
• Familiarity with embedding models (hosted or local): OpenAI, Cohere etc or HuggingFace models / sentence-transformers.
• Solid understanding of data modeling, warehousing, and performance optimization.
• Experience with messaging middleware + streaming (e.g. NATS Jetstream, Redis Streams, Apache Kafka or Pulsar etc.)
• Hands-on experience with data lakes, lakehouses, or components of the modern data stack.
• Exposure to MLOps tools and best practices.
• Exposure to workflow orchestration frameworks (e.g. Metaflow, Airflow, Dagster)
• Exposure to Kubernetes
• Experience working with unstructured data (e.g., logs, documents, images).
• Awareness of data governance, privacy standards, and high-security environments.
• Familiarity with containerization tools like Docker and/or Kubernetes.

Success Metrics

• Deliver reliable and scalable data pipelines that support production-level machine learning systems.
• Maintain high standards for data quality, uptime, and infrastructure performance across all platforms.
• Accelerate AI and product team velocity by ensuring fast, dependable access to clean, well-structured, and query-efficient data.

Why Join Us?

• Contribute to cutting-edge AI products with meaningful real-world impact.
• Be part of a collaborative, innovative team with opportunities to learn and grow.
• Enjoy flexible work arrangements, including remote/hybrid options and regular team meetups in London.
• Receive a competitive salary and benefits.