Senior Data Engineer Greece

Architect and build production data pipelines and data platforms that serve models, data, and AI workflows to internal and client-facing applications, accountable for them under live traffic.

Own non-functional quality — latency and throughput budgets, scalability, reliability, observability, and cost — for the systems in your domain.

Lead the design and operation of multi-model data stores — relational databases (PostgreSQL, MySQL), vector databases (Pinecone, Weaviate, pgvector), and graph databases (Neo4j, Neptune) — ensuring the right tool for each access pattern.

Set technical direction : write design docs, make build-vs-buy decisions, and defend your approach with evidence.

Work across the stack when needed — services, data access, infrastructure-as-code, CI/CD — and debug it when things drift in production.

Mentor and set the quality standards for mid-level and junior data engineers.

5+ years of professional software engineering experience, shipping and operating production systems — you’ve dealt with scaling, reliability, on-call, and the gap between a working prototype and a dependable service.

Deep, demonstrable expertise designing and building distributed data pipelines with Apache Spark , and strong data modelling across relational, vector, and graph databases .

Strong proficiency in at least one general-purpose language (e.g., Python, Scala, or Java) and the ability to work effectively across others.

Hands-on experience with cloud platforms (GCP/AWS), containers (Docker), CI/CD, and infrastructure-as-code (Terraform).

Strong software engineering habits — version control, testing, code review, CI/CD.

Comfort with ambiguity. Many of our problems don’t have a known-good solution.

Clear communication — you can write a one-page design doc that is useful for both product managers and staff engineers.

Experience building and operating ML/LLM-powered production systems (model serving, RAG, agents) at scale.

Experience with event-driven or streaming architectures (e.g., Pub/Sub, Kafka) and real-time systems.

Depth in security, IAM, networking, and data governance in cloud environments.

Background in marketing technology, ad tech, or large-scale data products.

Meaningful open-source contributions or a track record of technical leadership.