DATA ENGINEER (Data Science & Big Data Analytics
Role details
Job location
Tech stack
Job description
You will join the Big Data & Data Science unit, a diverse team covering areas as varied as Computational Social Science, Cognitive Neuroscience, and Trustworthy AI. We are looking for an intelligent and curious data engineer to help us translate applied research into tangible products and prototypes, working on real European research projects alongside researchers, software engineers, and project managers., * Design, build, and maintain data pipelines (batch and streaming) that ingest data from heterogeneous sources into data lakes and warehouses, including metadata and lineage tracking.
- Contribute to the development of federated query and discovery systems over distributed datasets (UNCAN.eu), working with engines such as Trino and integrating query optimizers compliant with privacy requirements.
- Contribute to the deployment of European data spaces (DeployEMDS) using standard building blocks from IDSA, Gaia-X, and FIWARE, including data catalogues, brokers, and connectors.
- Build and maintain orchestration workflows using Airflow or Dagster, following software engineering best practices (tests, code review, CI/CD).
- Package and deploy services using Docker and Docker Compose or similar
- Support Machine Learning projects with data storage, serving, and versioning infrastructure (object storage, SQL/NoSQL databases, feature stores).
- Collaborate on multi-cloud and on-premise deployments (e.g. Hetzner, Azure, bare metal) and contribute to infrastructure-as-code practices.
- Support the preparation of technical sections in EU-funded project proposals (Horizon Europe and similar), and contribute to scientific dissemination (papers, prototypes, demos).
Requirements
MSc in Computer Science, Data Engineering, Mathematics, Physics, or related technical field. A PhD or specialised Master's will be highly valued.
Experience
At least 2 years of professional experience as a Data Engineer or in a closely related role
Technical skills
- Strong Python proficiency, including modern tooling for clean code (type hints, linters/formatters such as Ruff, testing with pytest).
- Solid SQL skills and experience with relational databases (PostgreSQL, MySQL)
- Experience with at least one NoSQL or document database (Redis, Elasticsearch, or similar)
- Experience building ETL/ELT data pipelines (Airflow, Dagster or similar)
- Working knowledge of object storage (S3, MinIO) and common serialization formats (Parquet, JSONL, Avro, BSON).
- Comfort on Linux and with the command line
- Docker and Docker Compose for packaging and local development
- Git and CI/CD workflows (GitHub Actions, GitLab CI, or similar)
- Understanding of batch vs. streaming paradigms and event-driven architectures
- Understanding of the difference between Data Lake and Data Warehouse architectures, and when to use each.
Languages
- Excellent written and spoken English
- Knowledge of Catalan and/or Spanish is a plus, * Experience with distributed query engines (Trino, Presto, Dremio) and the concept of federated queries over heterogeneous data sources.
- Familiarity with European data spaces initiatives: IDSA, Gaia-X, FIWARE, DSSC, Eclipse Dataspace Components; data catalogues (CKAN), brokers, and connectors.
- Big Data ecosystem: Apache Spark, Flink, Kafka, RabbitMQ, Hadoop
- Kubernetes and Helm for production deployments
- Infrastructure as Code with Terraform, Ansible, or similar
- Observability stacks: OpenTelemetry, Prometheus + Grafana, Loki, or equivalents
- Experience with cloud providers (Azure, AWS, GCP, Hetzner): serverless functions, managed storage, IAM.
- Graph databases (Neo4j) or time-series databases
- Machine Learning fundamentals and familiarity with ML lifecycle tooling (MLflow, feature stores, model versioning).
- Concurrency and backend knowledge: async programming, multithreading, actor model, message-driven systems.
- Additional programming languages: Java, Scala, Go, or Rust
- Participation in EU-funded research projects (Horizon Europe, Digital Europe) or scientific publications / conference presentations.
- Relevant certifications (cloud providers, Kubernetes CKA/CKAD, data platforms)
Benefits & conditions
- Hybrid work (home office/ work in the office).
- Flexible Schedule.
- Shorter workday on Friday and Summer Schedule.
- Flexible remuneration package (health insurance, transport, lunch, studies - training and kindergarten).
- Eurecat employees can join the Eurecat Academy courses.
- Language courses (English, Catalan and Spanish).
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