• Design, develop, and maintain enterprise data architecture solutions that support business growth, operational efficiency, regulatory compliance, and risk management objectives. • Lead modernization initiatives across data platforms, integration frameworks, reporting environments, and analytical capabilities. • Evaluate and implement emerging technologies, including cloud services, artificial intelligence, machine learning, automation, and advanced analytics solutions where appropriate. • Architect scalable and reusable data integration frameworks that reduce maintenance overhead and support future business expansion. • Develop and optimize ETL/ELT processes, data pipelines, and data movement strategies across multiple systems and platforms. • Collaborate with business leaders, risk management teams, compliance personnel, and technology stakeholders to translate business requirements into sustainable technical solutions. • Design data models, metadata strategies, governance frameworks, lineage tracking, and quality controls that support enterprise reporting and regulatory requirements. • Partner with stakeholders to identify opportunities for process automation, operational improvement, and enhanced decision-making through data-driven solutions. • Establish and promote development standards, architectural best practices, and data engineering disciplines that balance agility with long-term maintainability. • Evaluate existing systems and processes to identify technical debt, scalability limitations, operational risks, and modernization opportunities. • Support risk modeling, forecasting, financial analytics, and strategic reporting initiatives through robust data architecture and engineering practices. • Implement monitoring, validation, reconciliation, and control processes to ensure data accuracy, integrity, availability, and auditability. • Participate in technology roadmap planning and provide architectural guidance for future-state platform evolution. • Serve as a technical leader and trusted advisor across multiple business and technology teams. • Maintain awareness of regulatory expectations, industry trends, cybersecurity considerations, and emerging technologies impacting financial services organizations.

• Design and develop CLI tools, scripts, and internal utilities to automate repetitive tasks across the data platform, including: • Pipeline execution and orchestration • Data governance workflows • Metadata synchronization • Environment setup and configuration • Test harness development • Automate workflows on Databricks, including: • Job deployment and scheduling • Environment provisioning • MLOps processes using APIs, Terraform, or Databricks SDK • Build and implement robust testing frameworks: • Integration testing for pipelines • End-to-end validation of ETL/ELT workflows • Testing and validation for ML inference workflows • Improve overall productivity, scalability, and reliability of the data and ML engineering ecosystem • Develop lightweight internal tools and dashboards using frameworks such as React, Streamlit, or similar technologies to: • Visualize data pipelines and workflows • Demonstrate model inference capabilities • Provide configuration and operational controls • Enable internal productivity monitoring and dashboards • Collaborate with cross-functional teams to identify automation opportunities and implement best practices

• Definir y poseer modelos de datos impulsados por el negocio para la elaboración de informes financieros • Actuar como enlace entre los interesados en Finanzas y los equipos de tecnología • Diseñar y supervisar flujos de datos de extremo a extremo

Role Description We are looking for Senior Data Engineers to join a project delivered for our international client operating in a data-intensive and technology-driven environment. The client is building modern real-time data platforms that power operational and near real-time business use cases such as logistics, tracking, fraud detection, customer interactions, and intelligent decision-making systems. This role is a great opportunity to work on large-scale event-driven architectures and streaming platforms processing high volumes of data with strict latency and reliability requirements. You will contribute to the design, development, and optimization of real-time data pipelines and distributed processing systems that enable business-critical applications and analytics capabilities. About project: - The project combines data engineering, distributed systems, cloud technologies, and streaming architectures within a collaborative international environment. - You will work closely with platform engineers, machine learning teams, product stakeholders, and other engineering disciplines to deliver scalable and reliable real-time data solutions. You will be: - Designing and implementing real-time data pipelines and streaming architectures. - Building, maintaining, and improving event-driven data processing systems. - Ensuring low-latency and high-throughput processing of streaming data at scale. - Integrating multiple internal and external data sources into streaming pipelines. - Implementing monitoring, alerting, logging, and observability solutions for real-time systems. - Ensuring data quality, consistency, and reliability within streaming environments. - Collaborating closely with platform, machine learning, and product teams on real-time business use cases. - Optimizing pipeline performance, scalability, and operational stability. - Participating in architecture discussions related to distributed systems and event-driven platforms. - Supporting troubleshooting, debugging, and continuous improvement activities across data processing environments. - Contributing to engineering best practices, automation, and operational excellence initiatives. - Depending on seniority, taking ownership of technical areas, supporting architectural decisions, and mentoring less experienced team members. Qualifications - 5+ years of commercial experience in Data Engineering or related disciplines. - Strong programming skills in Python, Java, or Scala. - Hands-on experience with streaming technologies such as Kafka, Kinesis, Google Pub/Sub, or similar platforms. - Proven experience building real-time or near real-time data pipelines. - Strong understanding of distributed systems and event-driven architecture principles. - Practical experience working with AWS cloud services. - Experience with modern data processing frameworks such as Apache Flink, Spark Streaming, or similar technologies. - Strong understanding of scalability, fault tolerance, and reliability in distributed environments. - Experience working with modern software engineering practices, testing approaches, and CI/CD workflows. - Strong problem-solving, communication, and collaboration skills. - Ability to work effectively in cross-functional and international teams. - Experience with stateful stream processing and windowing concepts. - Understanding of exactly-once and at-least-once processing semantics. - Familiarity with monitoring, observability, and operational tooling for streaming platforms. - Experience integrating streaming pipelines with downstream analytics, reporting, or machine learning solutions. - Exposure to containerization and orchestration technologies such as Docker and Kubernetes. - Practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery. - Work from the European Union region and a work permit are required. Requirements - Experience with unified batch and streaming platforms such as Databricks, Apache Beam, or similar technologies. - Knowledge of real-time machine learning feature pipelines and feature engineering workflows. - Experience working with high-scale event-driven systems processing millions of events per day. - Experience contributing to platform-level architecture and technical strategy. - Familiarity with data platform modernization and cloud-native architecture patterns. - Experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work. - Interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches. Recruitment Process - CV review - HR call - Interview - Client Interview - Decision