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# Data Research Engineer

- **Company:** Nuevo
- **Location:** Barcelona, Spain
- **Contract:** Permanent contract
- **Skills:** Extract Transform Load (ETL), Distributed Computing Environment, Python (Programming Language), Machine Learning, NumPy, Open Source Technology, Software Engineering, Data Processing, Data Storage Technologies, Delivery Pipeline, Large Language Models, Apache Spark, Pandas, Information Technology, Dask, Machine Learning Operations, Data Generation
- **Published:** September 24, 2026
- **Apply:** https://www.adzuna.es/contact-us.html

## About the Role

o Identifying good data sources to train and evaluate ML models, including real-world and realistic synthetic data sources
Bringing data from structured and unstructured sources, as well as simulators and causal models, into formats accessible by ML models

 + Strong fundamentals of software engineering
 + Strong knowledge of:
o Python
o Python data processing stack (numpy, pandas, …)

 + Familiarity with:
o distributed processing (e.g. Ray, Dask Spark, Beam)
o data storage solutions

 + Basic ML knowledge, + Contributions to open source ML projects
 + BSc/MSc/PhD in computer science/machine learning
 + Experience working with tabular data / predictive analytics
 + Experience working with "classical machine learning and deep learning" (pre-LLM)
 + Experience working with synthetic data generation, Structured Causal Models, or physical / systems-based simulators

## Description

The greatest research is done through solid engineering. As part of the research team, you will contribute to development of breakthrough machine learning models by working on one of the most crucial aspects of ML model training: data. The main responsibilities of this role are:

 + Helping to identify, characterize and evaluate data sources, including realistic synthetic data generated from Structured Causal Models and physical / systems-based simulators
 + Building and maintaining ETL pipelines
 + Designing and implementing scalable, reliable data storage solutions
 + Collaborating with the rest of the research team to maintain a reliable, efficient training pipeline where data is a critical component
 + Collaborating with the wider engineering and infrastructure team