Mid Level Data Engineer
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Job Description
Mid-level Data Engineer building reliable pipelines, data models, and infrastructure for Satalia’s data science, optimization, and AI projects. Fully remote from Greece.
Responsibilities:
- Build and own pipelines and data models that turn raw data into clean, reliable, well-documented datasets
- Build robust ETL/ELT pipelines that ingest, transform, and serve large datasets across the marketing intelligence stack
- Design well-documented, tested, version-controlled dbt data models powering downstream AI and BI
- Model data in the warehouse/lakehouse and relational databases, choosing structures for each access pattern
- Implement orchestration, monitoring, data-quality checks, and lineage
- Design, build, and maintain production data pipelines for analytics, BI, and AI workflows
- Build SQL-based transformations and models into documented, tested datasets
- Design and operate data warehouses/lakehouses and relational databases such as PostgreSQL
- Contribute to pipeline infrastructure, orchestration, CI/CD, and infrastructure-as-code for the data platform
- Work closely with data scientists and software engineers
- Own pipelines end to end
- Share knowledge with the team
Requirements:
- 4+ years building and running production data pipelines and data models
- Strong SQL and data modelling, including SQL-based transformation (dbt or similar)
- Solid experience with a data warehouse or lakehouse
- Strong Python; Scala or Java is a plus
- Good software engineering habits, including version control, testing, code review, and CI/CD
- Experience with pipeline orchestration such as Airflow, Dagster, or similar
- Hands-on experience with a cloud platform such as GCP or AWS
- Ability to write a short design document useful to product managers and engineers
- Nice to have: distributed data processing at scale, such as Apache Spark
- Nice to have: containers, including Docker, and infrastructure-as-code, including Terraform
- Nice to have: vector databases such as Pinecone, Weaviate, or pgvector, or graph databases such as Neo4j or Neptune
- Nice to have: exposure to ML/LLM-powered data systems, including RAG, embeddings, and feature/serving pipelines
- Nice to have: event-driven or streaming data, including Pub/Sub, Kafka, and real-time pipelines
Benefits:
- Healthcare
- Remote working — café, bedroom, beach — wherever works
- Truly flexible working hours
- Generous leave in line with Greek Labour Law
- Impactful projects focused on meaningful social and environmental change
- People-oriented culture with wellbeing as a priority
- Transparent and open culture
- Development opportunities
- Autonomy and freedom
- Culture of inclusion and warmth
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