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# Technical Data Analyst

- **Company:** Mrm Madrid
- **Location:** Madrid, Spain
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Airflow, Data Analysis, Information Engineering, Extract Transform Load (ETL), Data Warehousing, Database Testing, Monitoring of Systems, Python (Programming Language), NumPy, Standard Sql, Software Engineering, Data Ingestion, Flask (Web Framework), Delivery Pipeline, Git, Fastapi, Pandas, Containerization, Software Version Control, Data Pipelines, Docker
- **Published:** July 14, 2026
- **Apply:** https://www.jobleads.com/es/job/ea1ccdd196c3ca77564a4a9de4d205dd8

## About the Role

* Advanced proficiency in Python with a strong focus on data manipulation and optimization (Pandas, NumPy, production-ready scripting).
 * Expert-level SQL skills for querying, extracting, transforming, and modeling complex data in data warehouses.
 * Solid experience building and automating data ingestion and processing pipelines using ETL/ELT tools such as Airflow, dbt, or similar.
 * Familiarity with deployment workflows, APIs (FastAPI/Flask), containerized environments (Docker), and version control systems (Git).
 * Proven ability to analyze metrics, formulate hypotheses, and communicate effectively with both technical and business stakeholders.

## Description

* Technical Support & Production Deployment: Work closely with the Advanced Analytics team to transform prototypes, scripts, and experimental models (notebooks) into automated, stable, efficient, and production-ready services.
 * Data Engineering & ETL/ELT: Design, optimize, and maintain robust data pipelines. Ensure data sources are properly structured, clean, and continuously available so analytical models can consume high-quality data.
 * Analytical & Business Insight: Actively participate in the analysis phase by understanding the analytical objectives of each project, performing exploratory data analysis, validating data quality, and ensuring technical outputs address specific business questions.
 * Robustness & Best Practices: Introduce software engineering standards into the analytics workflow, including version control, data testing, modular development, and technical documentation.
 * Model Monitoring: Support the monitoring of variables feeding deployed models to detect data drift and ensure long-term model performance.


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