Data Engineer (Spanish Speaker) - Remote Work Type

Directio

4d•Remote

About The Position

Directio is a global IT services company that consults, codes, tests, deploys, and manages cloud-based and mobile applications, providing around-the-clock support from offices in Poland, the Philippines, Mexico, and the USA. They prepare their FMCG, retail, automotive, and SaaS clients for the future by accelerating their digital transformation. The company operates under the “We Code Success” principle, prioritizing the success of clients, consultants, and partners. This specific role is for a Data Engineer for a Mexico client that specializes in building advanced tools for credit scoring analysis and fraud detection. Their solutions enable faster and more accurate identification of potential fraud risks during credit applications, supporting better financial decision-making.

Requirements

3+ years of experience as a Data Engineer, Azure Data Engineer, Data Platform Engineer or similar role, ensuring strong hands-on expertise in data engineering.
Willingness to work in night shifts - Necessary condition.
Experience with Microsoft Azure cloud services, enabling development and management of cloud-based data solutions.
Fluent Spanish language skills, enabling effective communication in international environments.
Experience with tools such as Azure Data Factory, Azure Databricks, Azure Data Lake Storage or Azure SQL Database, enabling end-to-end data processing.
Experience designing, developing and maintaining ETL or ELT workflows and scalable data pipelines, ensuring reliable data processing.
Experience with Python for data processing automation and transformations, enabling flexible and efficient data handling.
Knowledge of data architecture, data modeling and cloud solution design principles, enabling scalable and maintainable systems.
Experience with databases, data warehouses, data lakes or distributed data environments, enabling work with complex data ecosystems.
Ability to deploy, configure, monitor or support Azure-based data resources, enabling operational support of data platforms.
Ability to collaborate with data science, analytics, engineering and business teams, ensuring alignment and effective delivery.

Nice To Haves

Experience in fintech, banking, lending, credit scoring, fraud detection, risk analytics or financial services, enabling domain understanding of financial data.
Experience working with machine learning or data science teams, enabling support for advanced analytics use cases.
Experience with production-grade data platforms and cloud infrastructure, enabling work in enterprise environments.
Knowledge of data governance, data quality, security and compliance practices, enabling proper data management standards.
Experience with monitoring, performance tuning and optimization of data pipelines or cloud platforms, enabling improved system efficiency.
Certifications in data analytics, big data, cloud platforms or Microsoft Azure, demonstrating validated expertise.

Responsibilities

Designing, developing, and implementing efficient and scalable data pipelines, ensuring reliable extraction, transformation, and loading of data from multiple sources into data platforms using tools such as Azure Data Factory.
Building and maintaining modern data infrastructure, including storage layers, databases, processing environments, and monitoring solutions based on Microsoft Azure services.
Working with Azure Databricks and related platforms, supporting data processing, analytics, machine learning, and credit scoring use cases.
Optimizing the performance of data platforms, identifying bottlenecks and fine-tuning processes to ensure stability, scalability, and efficiency.
Supporting implementation of data security, compliance, and governance practices, ensuring data confidentiality, integrity, and availability.
Collaborating with data science, analytics, product, and engineering teams, translating data requirements into scalable and reliable technical solutions.
Supporting data workflows related to credit scoring, fraud detection, risk analytics, lending, and other fintech use cases.
Researching and evaluating new data engineering tools and technologies, recommending improvements to enhance performance and maintainability.
Creating and maintaining technical documentation including architecture diagrams, data flows, and pipeline configurations, ensuring clarity and knowledge sharing.