Key Skills

Job Description

About Crystalloids Crystalloids was founded in 2006 and consists of a team of experienced software and data specialists who help organizations innovate and grow by developing data-driven solutions. We are a Premier Google Cloud Partner. We believe successful solutions are built on three pillars: people, processes, and technology. At Crystalloids, we consider all three dimensions in everything we design and build for our clients. Over the years, we have built an impressive client portfolio across industries including retail, e-commerce, consumer packaged goods (CPG), media, travel, and leisure. Our clients include Rituals, KNVB, Body & Fit, FD Mediagroep, and ACSI. What Will You Do as an Analytics Engineer? At Crystalloids, we help organizations become truly data-driven and AI-driven by building scalable and efficient data platforms on Google Cloud Platform (GCP). As an Analytics Engineer, you will work at the intersection of data engineering, analytics, and AI. You will transform raw data into valuable insights using dbt and SQL, build reliable data models, and actively contribute to data engineering within the GCP ecosystem. AI is part of how you work At Crystalloids, AI is not a separate initiative or an occasional experiment. It is part of your daily engineering workflow. You will use modern AI tools to increase your development speed, automate repetitive work, accelerate debugging, generate and improve tests and documentation, explore solutions, and challenge your own technical approach. We expect our Analytics Engineers to continuously develop their ability to work effectively with AI — while critically validating AI-generated outputs and remaining fully responsible for the quality, security, and maintainability of what they deliver. You will learn how to combine strong engineering fundamentals with AI-assisted development to deliver better solutions faster. You will also contribute to creating AI value for our clients. Upstream, you will help validate AI use cases by testing assumptions, developing prototypes and Proofs of Concept (PoCs), and demonstrating potential business value. Downstream, you will help turn successful concepts into production-ready solutions by providing robust data models, reliable pipelines, governance, testing, documentation, and scalable implementation within Google Cloud. Are you excited about building well-modeled, tested, and documented datasets that create direct business value? Do you enjoy working with both technical and business stakeholders? And do you want to master how AI is changing modern analytics engineering? Then we would love to meet you. Your Responsibilities Data Modeling & Transformation Design and implement robust data models using dbt. Build conceptual data models using different modeling frameworks and methodologies. Work with business and technical stakeholders to identify, classify, qualify, and clearly define business concepts, their relationships, and business rules. Translate conceptual models and business definitions into logical and physical data models. Transform raw data into clean, documented, and reusable datasets for reporting, analytics, and AI applications. Build trustworthy data and semantic foundations that can be consumed by people, applications, and AI systems. Analytics Enablement Work with stakeholders to translate business requirements and business logic into technical solutions. Develop solutions that are traceable, testable, maintainable, and version-controlled. Help make organizational data understandable and usable for analytics and AI-driven applications. Development & Optimization Write efficient and maintainable code in SQL, Python, or another object-oriented programming language. Optimize data transformation pipelines for performance, scalability, and cost within BigQuery and other GCP services. Use AI-assisted development to accelerate coding, debugging, refactoring, testing, and technical problem-solving. Data Engineering Take ownership of data engineering activities, including: Developing and maintaining data pipelines. Building reliable data ingestion processes and ensuring stable pipeline execution. Working with Cloud Composer, Cloud Run, Pub/Sub, and Cloud Storage. Performing these activities independently rather than only supporting Data Engineers. AI-Native Engineering Use modern AI development tools as part of your daily engineering workflow. Apply AI to accelerate SQL and Python development, data modeling, debugging, testing, documentation, and code reviews. Use AI to explore alternative technical solutions and reduce repetitive engineering work. Develop effective prompting and context-engineering practices for technical work. Critically evaluate AI-generated code and recommendations rather than accepting outputs without validation. Continuously experiment with new AI capabilities and identify where they can improve engineering speed and quality. Take full ownership of the correctness, security, maintainability, and quality of AI-assisted work. AI Use Cases & Enablement Help clients identify where AI can create measurable business value. Rapidly validate assumptions through prototypes and Proofs of Concept. Help clients achieve AI quick wins and understand how successful experiments can be scaled. Prepare data foundations required for reliable AI and agentic solutions. Help move successful AI concepts from experimentation toward production. Testing & CI/CD Apply Analytics Engineering as Code principles. Implement automated testing, CI/CD pipelines, and version control using Git and, where relevant, Terraform. Use AI where appropriate to accelerate test generation, identify edge cases, improve code quality, and support documentation. Documentation & Data Governance Maintain complete documentation for data models, transformations, and datasets. Ensure transparency, reproducibility, data quality, and maintainability. Build data structures and documentation that can increasingly be understood and consumed by both humans and AI systems. Client Collaboration Present solutions and results to clients. Gather feedback and translate it into improvements to data products. Understand the client's business objectives rather than focusing only on technical implementation. Explain complex data and AI concepts clearly to both technical and non-technical stakeholders. Experience & Qualifications Education Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, or a comparable field. Professional Experience Good theoretical and relevant hands-on experience with: Data Modeling Data Engineering Data Analytics Cloud Hands-on experience with cloud technologies is important. If you have experience with another cloud platform and want to develop your expertise in Google Cloud, Crystalloids is the right place to make that transition. Already experienced with Google Cloud Platform (GCP)? Even better. Have a Google Cloud certification as well? We definitely want to hear from you — apply! Methodologies Data modeling methodologies and frameworks, including conceptual, logical, and physical data modeling. Dimensional modeling, such as star schemas and related analytical modeling approaches. Object-oriented design and UML for structuring and communicating technical concepts. CI/CD practices for automated testing, validation, and deployment. Personal Skills Strong analytical and problem-solving abilities. Strong communication skills. Experience working in multidisciplinary teams. Ability to work independently and take ownership. Careful and structured approach to documenting designs and processes. Curiosity and willingness to continuously experiment with better ways of working. Ability to combine speed with quality in an AI-assisted engineering environment. Other Requirements You live in the Netherlands. You have excellent spoken and written English. Dutch language skills are a plus. What Do We Offer? Work in an international environment with an engaged and social team. A competitive salary with an attractive bonus and pension scheme. Excellent work-life balance and up to 28 vacation days. Work from our modern Rotterdam office, with opportunities to visit clients on-site. A laptop and first-class public transport arrangement. Extensive opportunities for personal and professional development through technical training and education. Hands-on exposure to modern data, cloud, and AI technologies. An environment where you can continuously experiment with AI tools and develop an AI-native engineering mindset. The opportunity to learn how to use AI not only for experimentation, but to increase your everyday engineering productivity, speed, and impact.

Core Responsibilities

Design and implement tested, documented data models and transformation pipelines using dbt, SQL, Python, and Google Cloud services. Collaborate with clients to translate business needs into analytics and AI-ready data products, validate AI use cases, and help move successful prototypes into production.

Requirements

A bachelor's or master's degree in computer science, software engineering, data engineering, or a comparable field is requested, along with theoretical and hands-on experience in data modeling, data engineering, analytics, and cloud technologies. Candidates should have strong analytical, communication, and documentation skills, be able to work independently, live in the Netherlands, and have excellent spoken and written English; Dutch is a plus.

Benefits

• Competitive Salary
• Bonus Scheme
• Pension Scheme
• Up To 28 Vacation Days
• Work-Life Balance
• Laptop
• Public Transport Arrangement
• Professional Development And Training
• Exposure To Modern Data, Cloud, And AI Technologies