You will embed security by design across AI use cases, agents, and platform components while implementing controls for access, monitoring, and sandboxing. Additionally, you will drive risk remediation, manage AI-specific threats, and strengthen incident readiness through playbooks and cross-team collaboration.
You will own the business responsibility for the Marketing & Sales Data Product, defining the roadmap and establishing group-wide data standards. You will also collaborate with cross-functional teams to harmonize KPIs and support data-driven decision-making across the organization.
The Engineering Manager will own delivery execution and align teams with the technology roadmap while fostering a high-quality engineering culture. They will act as a line manager for engineers and serve as the technical counterpart to Product Owners within Agile teams.
Design, operate, and evolve scalable Kubernetes-based platforms while managing cluster lifecycles and automation workflows. Collaborate with cross-functional teams to implement CI/CD pipelines, GitOps practices, and robust monitoring frameworks.
The Data & Integration Architect will lead the definition and execution of data and integration strategies for a global migration from Veeva CRM to Salesforce. They will ensure alignment between business requirements and technical architecture across international stakeholders and delivery partners.
Implement and set up the Murex MRB module, including managing scenario containers and automated executions. Provide ongoing support for issues and incidents related to the Full Revaluation module.
Develop and maintain backend systems using Java and relational databases. Build and integrate REST and SOAP web services while following Agile methodologies.
Architect and build sophisticated AI agents using Gemini and CXAS, focusing on reasoning, grounding, and tool-use capabilities. Manage the migration of legacy conversational interfaces and implement RAG pipelines and CI/CD automation for scalable deployments.
Develop and maintain mobile test automation frameworks for iOS and Android using Appium and C#. Integrate automated tests into CI/CD pipelines via Azure DevOps and collaborate with stakeholders to translate requirements into testing solutions.
Provide functional application support within distributed microservices environments and handle API support. Perform frontend triage and utilize APM tools and logs for troubleshooting.
Model and transform data into consistent analytical structures while building and optimizing ELT pipelines. Collaborate with cross-functional teams to provide reliable semantic layers and ensure high data quality and observability.
Develop and maintain robust data pipelines and analytical data models using SSIS and SSAS. Build reporting solutions with Power BI and SSRS while optimizing SQL Server performance and ensuring data quality.
Build and maintain data pipelines for collecting, transforming, and integrating data from various sources. Ensure efficient data enablement and reporting solutions through the deployment of scalable data products.
The role involves refining GenAI user stories and aligning the GenAI scope with the Enterprise Architecture team. The engineer will assess the feasibility, risks, and security implications of GenAI implementations while acting as a technical partner for decision-making.
Lead the Automation Center of Excellence by ensuring the stability and evolution of hyperautomation solutions. Focus on transitioning existing automations toward advanced AI-driven models and agentification while defining governance and deployment standards.
The role involves designing and optimizing data flows within a Fabric Lakehouse environment, focusing on low-latency processing and multi-stack integration. Responsibilities include implementing data design patterns like SCD and managing transaction logs for Delta Lake.
Develop and maintain scalable data pipelines and manage solutions within the Microsoft Fabric Lakehouse environment. Collaborate with Data & AI teams to optimize cloud-based data solutions and ensure governance standards.
Define standards and blueprints for AI integration across the SDLC and DataOps while ensuring technical governance. Collaborate with cross-functional teams to maintain architectural consistency and support digital transformation initiatives.
The role involves performing test automation using Cucumber and BDD methodologies within a quality-focused team. The engineer will work in an Agile/Scrum environment to ensure software quality across various projects.
The role involves developing and implementing advanced backend automation solutions. The engineer will integrate these solutions into CI/CD pipelines using Docker.
The role involves joining a multidisciplinary Agile team to develop and maintain automated tests. The focus is on ensuring software quality through automation within an international project environment.
Design and develop scalable backend platforms and reusable APIs, as well as implement cloud-native and containerized applications. Collaborate with cross-functional teams to translate business requirements into technical solutions.
Promote QA best practices and automation while identifying continuous improvement opportunities in testing and delivery processes. Contribute to AI-driven testing initiatives and help define quality standards and governance.
Develop analytical and machine learning models to create measurable business value. Collaborate with diverse teams to craft impactful data-driven solutions across the full analytical lifecycle.
Design and execute advanced analytical and machine learning solutions while linking technical decisions to business objectives. Mentor mid and junior engineers to ensure technical excellence and drive high-quality decision-making across cross-functional groups.
Design and document architectural solutions for application and integration challenges, ensuring alignment with functional and technical requirements. Act as the primary architectural reference point throughout the project lifecycle and collaborate with stakeholders to align decisions.
Define and design end-to-end technology architectures to ensure solutions are scalable, secure, and aligned with business objectives. Translate business needs into technical requirements and produce architectural artifacts such as high-level designs and API models.
Lead the design and implementation of end-to-end cloud-native data architectures, including ingestion, modeling, and orchestration. Provide technical leadership by mentoring engineers, optimizing pipelines, and communicating complex decisions to stakeholders.
The candidate will develop and maintain ETL/ELT pipelines using Azure Data Factory, Databricks, and PySpark. They will also support data preparation and validation within Data Lake and Lakehouse environments.
Develop and maintain fullstack web applications using .NET and JavaScript. Responsible for creating back-end procedures, managing SQL databases, and interpreting end-user requirements into technical solutions.
Develop and implement AI solutions by designing proof-of-concepts and translating them into minimum viable products. Collaborate with cross-functional teams to optimize AI models and integrate capabilities into existing systems.
The role involves applying secure development practices and ensuring compliance with European information security regulations. The expert will implement security frameworks and manage risks within software and cloud environments.
Manage and maintain system infrastructure focusing on Ubuntu Linux, containerization, and CI/CD pipelines. Ensure system stability through monitoring, backup practices, and network management.
Develop and maintain robust data pipelines and analytical data models using SSIS, SSAS, and SQL Server. Build reporting solutions with Power BI and SSRS while collaborating with business teams to translate requirements into technical solutions.
