Data Engineer Greece - Mid Level
Role details
Job location
Tech stack
Job description
Our current work includes:
- Large-scale data processing - building robust ETL/ELT pipelines with Apache Spark that ingest, transform, and serve terabyte-scale multimodal datasets across the marketing intelligence stack.
- Polyglot data infrastructure - working with relational databases for transactional workloads, vector databases for semantic search and RAG systems, and graph databases for audience relationship and attribution modelling.
- Data transformation and modelling - using dbt to create well-documented, tested, and version-controlled data models that power downstream AI and BI systems.
- Data platform reliability - building the data platform foundations (orchestration, monitoring, data quality checks, lineage tracking) that ensure our pipelines and databases are dependable under production load.
You will build and operate the services, APIs, and infrastructure that turn our models and data into reliable products, working closely with data scientists and data engineers to ship and run systems in production. You'll have real ownership of your work - designing services, shipping code, and operating what you build - with guidance from senior engineers who will help you grow technically.
What you'll be doing:
- Design, build, and maintain data pipelines that serve models, data, and AI workflows to internal and client-facing applications.
- Work across database types - relational, vector, and graph - to model and store data appropriately for each access pattern, in partnership with data scientists.
- Build and maintain dbt models - writing transformation logic, tests, and documentation that ensure data quality and traceability.
- Operate what you build: instrument pipelines with logging, metrics, and tracing, and help diagnose and resolve production data issues.
- Write clean, tested, production-quality code and contribute to CI/CD pipelines and infrastructure-as-code.
- Participate in code reviews, design discussions, and retrospectives.
Requirements
- 2-4 years of professional data engineering experience, with work deployed to production.
- Strong proficiency in at least one general-purpose language (e.g., Python, Scala, or Java) and comfort working across a codebase.
- Solid fundamentals in designing and building data pipelines (ETL/ELT), with experience in Apache Spark for large-scale data processing.
- Experience with cloud platforms (GCP/AWS), containers (Docker), and CI/CD.
- Strong software engineering practices - Git, testing, code review, CI/CD.
- Clear communication - you can explain technical choices and trade-offs to both technical and non-technical colleagues., * Experience integrating ML/LLM systems into production applications (model serving, RAG, agents).
- Familiarity with infrastructure-as-code (Terraform), orchestration, and observability tooling.
- Experience with event-driven or streaming architectures (e.g., Pub/Sub, Kafka).
- Exposure to security, IAM, and data governance in cloud environments.
- Background in marketing technology, ad tech, or large-scale data products.
Benefits & conditions
Pulled from the full job description Paid volunteer time Flexible schedule, * Benefits - healthcare
- Remote working - café, bedroom, beach - wherever works;
- Truly flexible working hours - school pick up, volunteering, gym;
- Generous Leave - inline with Greek Labour Law
- Impactful projects - focus on bringing meaningful social and environmental change;
- People oriented culture - wellbeing is a priority, as is being a nice person;
- Transparent and open culture - you will be heard;
- Development - focus on bringing the best out of each other;
Satalia is home to some of the brightest minds in AI and if you're looking to join a company who not only values autonomy and freedom, but embraces a culture of inclusion and warmth, we'd love to hear from you.