Data Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Engineer based in Switzerland. This is an opportunity to join a remote-first engineering environment and help build the data infrastructure powering a fast-growing global SaaS business. You will design and maintain scalable data pipelines, transformation processes, and data models that support internal products, reporting, and client-facing outcomes. Working closely with Product, Design, Leadership, and Engineering teams, you will translate business needs into reliable and discoverable data solutions. The role offers meaningful ownership as the data platform continues to evolve and scale. You will have the freedom to research, make technical decisions, and improve engineering practices while maintaining a strong focus on data quality and performance. It is an ideal role for a proactive Data Engineer who enjoys autonomy, collaboration, and solving complex data challenges. Design, build, and maintain scalable and reliable data pipelines using Python, PySpark, and SQL. Develop, optimize, and maintain ETL and data transformation processes to deliver accurate and relevant information for internal reporting and products. Collaborate with Product Managers, Designers, Leadership, and Engineering teams to understand requirements and translate them into effective data solutions. Build scalable, well-structured, and discoverable data models using SQL, supported by clear and comprehensive documentation. Implement data quality checks, monitoring, and validation processes to maintain data accuracy, consistency, and integrity. Optimize data storage and retrieval processes for performance, scalability, and cost efficiency. Contribute to the optimization and maintenance of data-processing clusters and ensure efficient resource utilization. Establish and promote data engineering best practices across processing, modeling, documentation, and development workflows. Document technical processes, data models, and engineering practices clearly for internal stakeholders and, where appropriate, client-facing audiences. Continuously improve team workflows, engineering processes, and development practices. Take ownership of individual objectives and contribute to broader team and business goals through measurable outcomes. Proactively investigate data challenges, conduct research, make informed technical decisions, and drive solutions independently. Requirements: 2–4 years of professional experience in a Data Engineering role, ideally involving internal, financial, or operational reporting. Strong proficiency in Python, including dataframes, object-oriented programming, modularity, and maintainable code practices. Practical experience with PySpark and large-scale data processing. Strong SQL skills, including the ability to write complex queries, data transformations, and data quality checks. Proven experience with data modeling and query optimization. Experience building or maintaining scalable data infrastructure in a growing SaaS or technology environment is highly desirable. Experience with Databricks and dbt is a strong advantage. Familiarity with databases such as ClickHouse is a plus. Understanding of DevOps principles and CI/CD practices is advantageous. AI fluency and familiarity with emerging AI development concepts such as MCP, agents, and cross-agent review is a plus. Strong analytical and problem-solving abilities, with creativity, independent thinking, and a proactive approach to technical challenges. Ability to take ownership, conduct independent research, and make sound conclusions in an evolving environment. Strong collaboration and communication skills, with the ability to work effectively with engineers, product teams, designers, leadership, and other stakeholders. Strong organizational and time-management skills, including the ability to prioritize work independently and manage tight deadlines. Comfort