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# Data Engineer

- **Company:** Esp-corp Madrid-jll Madrid/castellana 79
- **Location:** Madrid, Spain (Remote available)
- **Contract:** Permanent contract
- **Skills:** Algorithmic Trading, Amazon Web Services, Business Analytics Applications, Audit Trail, Automation of Tests, Microsoft Azure, Big Data, BigQuery, Cloud Computing, Cloud Database, Databases, Data Validation, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Systems, Data Warehousing, DevOps, Dimensional Modeling, Document-Oriented Databases, Job Scheduling, Python (Programming Language), Performance Tuning, SQL Databases, Data Processing, Google Cloud, Data Ingestion, Snowflake, Apache Spark, Multi-Cloud, Data Lakes, Pyspark, Information Technology, Google Bigquery, Data Pipelines, Databricks
- **Published:** July 20, 2026
- **Apply:** https://www.adzuna.es/contact-us.html

## About the Role

+ Bachelor's degree in Computer Science, Data Engineering, or a related field (Master's degree preferred)
 + Minimum 3-5 years of experience in data engineering or full-stack development, with a focus on cloud-based environments
Technical Skills:

 + Strong expertise in managing big data technologies (Python, SQL, PySpark, Spark) with a proven track record of working on large-scale data projects
 + Strong Databricks experience
 + Strong database/backend testing with the ability to write complex SQL queries for data validation and integrity
 + Strong experience in designing and implementing data pipelines, ETL processes, and workflow automation
 + Familiarity with data warehousing concepts, dimensional modeling, data governance best practices, and cloud-based data warehousing platforms (e.g., Google BigQuery, Snowflake)
 + Experience with cloud platforms such as Microsoft Azure, or Google Cloud Platform (GCP)
 + Experience working in DevOps model
 + Experience with Unit, Functional, Integration, User Acceptance, System, and Security testing of data pipelines
Core Competencies:

 + Strong problem-solving skills and ability to analyze complex data processing issues
 + Excellent communication and interpersonal skills to collaborate effectively with cross-functional teams
 + Attention to detail and commitment to delivering high-quality, reliable data solutions
 + Ability to adapt to evolving technologies and work effectively in a fast-paced, dynamic environment

## Description

We are seeking a Data Engineer P2 who is a self-starter to work in a diverse and fast-paced environment as part of our Capital Markets Data Engineering team
This individual contributor role is responsible for designing and developing data solutions that are strategic to the business and built on the latest technologies and patterns
This is a global role that requires partnering with the broader JLLT team at the country, regional, and global levels by utilizing in-depth knowledge of data, infrastructure, technologies, and data engineering experience, + Design and implement robust, scalable data pipelines using Databricks, Apache Spark, and Delta Lake as well as BigQuery

 + Design and implement efficient data pipeline frameworks, ensuring the smooth flow of data from various sources to data lakes, data warehouses, and analytical platforms
 + Troubleshoot and resolve issues related to data processing, data quality, and data pipeline performance
 + Document data infrastructure, data pipelines, and ETL processes, ensuring knowledge transfer and smooth handovers
 + Create automated tests and integrate them into testing frameworks
Platform Engineering

 + Configure and optimize Databricks workspaces, clusters, and job scheduling
 + Work in a Multi-cloud environment including Azure, GCP and AWS
 + Implement security best practices including access controls, encryption, and audit logging
 + Build integrations with market data vendors, trading systems, and risk management platforms
 + Establish monitoring and performance tuning for data pipeline health and efficiency
Collaboration & Mentorship

 + Collaborate with cross-functional teams to understand data requirements, identify potential data sources, and define data ingestion
 + Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver data solutions that meet their needs


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