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C++ Low-Latency Market Data Engineer (Barcelona) - Hybrid
Builds high-performance C++ software for low-latency trading platforms used across Europe, focusing on real-time market data, networking, and distributed architectures. Hybrid role based in Barcelona, with relocation support and a negotiable salary.
Data Engineer (Hybrid) - Málaga, Python/Spark/Airflow
Data Engineer in Málaga (hybrid, 2–3 days/week on-site) developing and maintaining scalable data solutions on cloud data platforms. Core stack is Python, Apache Spark, and Airflow, with 8+ years' experience and B2 English required for team collaboration.
Data Engineer (Spark, Scala, Java, Azure Databricks)
Data Engineer designing, building and supporting data lake pipelines with Apache Spark, Scala, SQL, Azure Databricks and AWS EMR, orchestrated via Airflow and Control-M, for market risk use cases in an international environment. 100% remote role, minimum 2 years experience.
Data Engineer (Java + Spark) con inglés (Teletrabajo)
Java/Spark data engineer working 100% remotely from Spain on a stable project optimizing calculation and data-processing pipelines, improving application performance, and ensuring code quality alongside data and quantitative specialists. Core stack: Java, Apache Spark, Parquet, JUnit, Git, CI/CD (AWS and quantitative-finance knowledge a plus).
Data Engineer - Azure & Snowflake (Hybrid/Remote)
Data Engineer building, evolving and maintaining data integration and transformation pipelines with Azure and Snowflake, including change control and monitoring to ensure data quality. Onsite in Barcelona for the first 2-3 weeks, then hybrid with the possibility of full remote.
Python & PySpark Data Engineer — Databricks, Remote
Data Engineer at Hopla Software who designs, develops, and maintains ETL/ELT pipelines in distributed Big Data environments, writing clean Python code and optimizing workloads with Databricks and PySpark for efficient resource use.
Data Engineer — Hybrid ML & Credit Risk Pipelines
Data Engineer in Barcelona building and maintaining production-grade data products and ML pipelines for credit risk and fraud models. Owns the ML lifecycle and builds APIs exposing scoring, risk signals, and underwriting features for the company's credit platform.
