Kumulus

• Managing the product backlog, ensuring prioritization is aligned with business objectives • Creating and detailing user stories with clear acceptance criteria • Facilitating communication between stakeholders and the development team, becoming the client’s Trusted Advisor • Defining and monitoring KPIs to measure product success • Conducting alignment meetings with stakeholders to understand requirements and scope adjustments • Maintaining a clear and accessible product vision for the entire team • Ensuring proper documentation and alignment of all project items • Supporting and managing user acceptance testing (UAT) phases • Being responsible for boards, task management, and tracking the team’s activities • Performing validations and tests together with the QA team to ensure delivery quality • Monitoring and supporting the client whenever necessary • Assisting in planning and defining deadlines together with the team • Preparing and leading executive meetings focused on roadmap, backlog, and delivery planning

• Provide technical leadership for projects, POCs, POVs, and MVPs in AI & Data • Translate business challenges into scalable, viable solution architectures • Define appropriate approaches for Machine Learning, classical AI, and GenAI according to each context • Design solutions for hybrid environments (on-premises + cloud) • Evaluate trade-offs between performance, cost, governance, and complexity • Serve as a technical reference and mentor more junior professionals • Support technical discussions with clients and internal teams

• Plan and execute manual and automated functional tests • Develop and maintain test automation scripts • Validate integrations and end-to-end flows • Identify, document, and track defects • Collaborate with the development team to ensure continuous quality • Execute performance tests using tools such as JMeter and BlazeMeter

• Develop and maintain APIs using Java and Spring Boot • Work with data persistence using Spring JPA • Implement security with Spring Security • Create automated tests (JUnit, ArchUnit) • Manage builds and dependencies with Maven • Use Docker for containerization • Version code and collaborate via Git

• Develop user interfaces using React.js and TypeScript • Ensure code quality with automated tests (Cypress) • Work on styling with Sass following UI/UX best practices • Collaborate with designers and backend developers for efficient integration • Version code and participate in code reviews using Git

• Define and evolve the data architecture on Databricks • Design and implement scalable, high-performance data pipelines • Ensure engineering best practices, governance, security, and observability • Optimize pipelines, clusters, and cloud costs • Support production troubleshooting and drive technical improvements • Serve as a technical reference for the team and the client

• Develop scripts and automations in Python / PySpark / SQL to modernize legacy processes (database management, rules, and alert engine), reducing manual effort and increasing traceability. • Develop end-to-end Machine Learning models: scoping, exploration, feature engineering, model selection/evaluation/interpretation, production deployment, monitoring and retraining. • Work with large volumes of data in distributed environments (Spark), with a focus on performance and data quality.

• Design, implement, and maintain data ingestion, processing, and storage solutions using Azure platform tools such as Azure Databricks, Azure Data Factory, Azure Data Lake, and SQL databases; • Develop and optimize ETL (Extract, Transform, Load) workflows using PySpark to ensure data integrity and quality; • Model and structure data efficiently to meet the organization's analytics and BI requirements; • Monitor and troubleshoot data infrastructure issues, ensuring system availability and scalability.