Are you ready to build reliable foundations for Data and AI solutions? Put your Python development and system integration experience to work on a data management platform that combines cloud technology, workflow orchestration, data governance, and Generative AI. As a medior engineer, you will deliver technical solutions independently within your area of responsibility and collaborate with engineers and data professionals on platform-wide improvements.
You & your role
As a Medior AI & Data Platform Engineer, you will develop, operate, and improve Rabobank’s data management platform. You will translate requirements into tested, maintainable solutions and take responsibility for implementation, integration, and operational follow-through within your scope. Using Python, APIs, orchestration tools, and cloud services, you will improve the platform’s reliability, scalability, and usability. You will also develop Generative AI functionality that strengthens data management capabilities and helps users work more effectively. Working with engineers, data governance specialists, data scientists, and product teams, you will resolve technical issues, contribute to shared engineering practices, and deepen your expertise across Data Engineering, AI Enablement, and Platform Engineering.
Examples from practice
- Design, build, test, and maintain automated data pipelines and integrations using Python, SQL, REST APIs, and orchestration tools to support data management and governance processes.
- Configure and improve metadata management, data lineage, business glossary, data quality, and governance capabilities in platforms such as Collibra, translating requirements into maintainable technical implementations.
- Develop and integrate tools and AI-enabled functionality that help data scientists and LLM engineers build, deploy, and operate Data and AI solutions.
- Implement, test, and improve workflows within Collibra to automate data governance processes and make them easier to maintain.
- Build and maintain integrations between Data and AI platforms, cloud services, enterprise systems, data sources, and internal APIs, addressing integration failures and data processing issues.
- Deploy, monitor, and troubleshoot Data and AI applications, including LLM-based applications, and improve their operational stability within your area of responsibility.
- Work with engineering, analytics, governance, and product teams to clarify requirements, assess implementation options, and deliver reliable technical solutions.
- Apply automated testing, version control, code reviews, documentation, and CI/CD practices to keep platform changes maintainable and ready for deployment.
Facts & figures
- 40 hours per week.
- Collaboration with multiple data platform and engineering teams across the bank.
- More than 49,000 Rabobank colleagues worldwide.
Top 3 responsibilities
- Independently deliver and maintain workflows, data pipelines, tooling, and integrations within your scope, using Python, REST APIs, orchestration tools, cloud services, and enterprise systems.
- Improve the data management platform’s metadata, lineage, business glossary, data quality, and governance capabilities while strengthening its reliability, scalability, and usability.
- Develop and operationalise Data and AI functionality, including Generative AI and LLM-based applications, through integration, deployment, monitoring, troubleshooting, automated testing, documentation, and CI/CD practices.
Together we achieve more
We combine diverse perspectives and technical expertise to build reliable, accessible foundations for Data and AI solutions. You will collaborate with engineers, data governance specialists, data scientists, analytics professionals, and product teams, contributing hands-on engineering experience and sharing knowledge through joint problem-solving and code reviews. Together, we improve Rabobank’s data management platform and develop Generative AI functionality that helps users work more effectively. You will have room to deepen your expertise while taking responsibility for technical delivery within your scope.
You & your talent
- One to three years of relevant experience in software engineering, data engineering, analytics engineering, platform engineering, or a related area, with demonstrable hands-on delivery of maintainable technical solutions. Relevant internships, traineeships, academic assignments, or personal projects may contribute to this experience when they demonstrate the required practical skills and ability to work independently within a defined scope.
- Strong hands-on Python development experience, including writing maintainable code and applying software engineering best practices.
- A solid understanding of REST APIs and system integration concepts, with the ability to build, test, and troubleshoot integrations.
- Working knowledge of SQL, databases, data modelling, and data processing concepts, sufficient to contribute to data pipelines and investigate data-related issues.
- Practical experience using Git, testing, documentation, and code reviews to deliver reliable, maintainable software.
- Strong analytical and problem-solving skills, with the ability to clarify requirements, evaluate implementation options, and independently drive technical solutions within your area of responsibility.
- Excellent communication and stakeholder management skills in English, including explaining technical choices and discussing requirements with technical and non-technical colleagues.
- Demonstrated ability to collaborate in multidisciplinary teams, share knowledge, and take responsibility for agreed technical deliverables.
- A proactive approach to learning and applying modern Data and AI technologies, supported by a willingness to investigate unfamiliar tools and share what you learn.
- Hands-on experience building, integrating, or operating solutions on Azure, Databricks, or comparable cloud and data platforms is an advantage.
- Experience developing or maintaining workflows and data pipelines with Airflow, Kedro, Azure Data Factory, or comparable orchestration tools is an advantage.
- Experience implementing metadata management, data governance capabilities, or workflows in Collibra or a comparable platform is an advantage.
- Practical experience building or integrating Generative AI functionality using LLMs, retrieval-augmented generation (RAG), vector databases, or prompt engineering is an advantage.
- Experience using CI/CD tooling, automated testing, monitoring, and logging to deploy or troubleshoot Data and AI applications is an advantage.
- Experience delivering technical work in an Agile team, including refinement, iterative delivery, and continuous improvement, is an advantage.
You & the application process
- If you are invited for an interview, Bo, our virtual assistant, will contact you via SMS and email to schedule it.
- Answers to frequently asked questions can be found at rabobank.jobs/nl/veelgestelde-vragen.
- A reliability check is part of the procedure.
- We respect your privacy.