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.

As a Data Engineer, you will play a key role in building and maintaining the data infrastructure that powers machine learning, analytics, and business <initiatives.You> will design scalable batch and near-real-time pipelines capable of handling millions of data changes every day.Your work will ensure data remains accurate, consistent, reliable, and readily available across the <organization.You> will collaborate closely with data scientists, MLOps engineers, product owners, and BI analysts to translate business needs into robust data solutions.The role combines hands-on engineering with opportunities to improve processes, architectures, integrations, and infrastructure <scalability.You> will work in a remote-first environment with flexible working hours and a strong focus on autonomy and collaboration.This is an opportunity to contribute to a rapidly growing, data-intensive environment while working with modern cloud and data technologies.

Accountabilities

Data pipeline development: Design, develop, test, optimize, and maintain scalable batch ETL and near-real-time data pipelines capable of processing high-volume data sources.

Data architecture: Build and evolve reliable data architectures that support machine learning, data science, business intelligence, and operational requirements.

Data quality: Ensure data is accurate, consistent, reliable, and fit for downstream analytical and operational use.

Scalability and optimization: Identify opportunities to improve internal processes, optimize data delivery, and redesign infrastructure to support increasing scale and complexity.

API integrations: Develop and maintain new API integrations to accommodate growing data volumes and evolving business requirements.

Cross-functional collaboration: Work closely with data scientists, MLOps engineers, product owners, and BI analysts to understand business processes, system architecture, and specific product needs.

Data infrastructure: Contribute to the development and maintenance of data platforms, databases, integrations, and cloud infrastructure supporting production workloads.

Requirements

• Education: Bachelor’s degree or equivalent practical experience in Computer Science, Engineering, Mathematics, or a related technical discipline.
• Professional experience: 3+ years of experience in data engineering, data platforms, business intelligence, or a related field.
• Data-centric applications: Proven experience implementing data warehouses, operational data stores, data integration solutions, or similar data-focused applications.
• Database expertise: Experience working with large-scale production relational and NoSQL databases.
• Data modeling: Strong understanding and practical experience with data modeling principles.
• Architecture knowledge: General understanding of modern data architectures and event-driven architectures.
• SQL: Strong proficiency in SQL for querying, transforming, and analyzing data.
• Programming: Familiarity with at least one scripting language, preferably Python.
• Data technologies: Hands-on experience with Apache Airflow and Apache Spark.
• Cloud platforms: Solid understanding of AWS data services, including S3, Athena, EC2, Redshift, EMR, EKS, RDS, and Lambda.
• Machine learning: Understanding of machine learning models is an advantage.
• Containerization: Familiarity with Docker, Kubernetes, or similar containerization and orchestration technologies is beneficial.
• Industry knowledge: Experience or knowledge of the gaming industry is a plus.
• Collaboration: Strong communication skills and the ability to work effectively with technical and business stakeholders across multiple disciplines.

Benefits

• Remote-first environment: Work remotely with a strong focus on flexibility, autonomy, and sustainable working practices.
• Competitive compensation: Competitive salary with individual performance-based bonuses paid quarterly.
• Paid leave: 28 days of paid annual leave.
• Flexible working hours: Core working hours from 10:00 AM to 3:00 PM in your local time zone, with flexibility outside these hours.
• Additional bonuses: Opportunities to earn referral bonuses and flash bonuses.
• Quality equipment: Access to high-quality, professional equipment to support effective remote work.
• Annual retreats: Company retreats designed to encourage collaboration, networking, and stronger connections across the team.
• Professional growth: Exposure to large-scale data engineering challenges, modern cloud technologies, and cross-functional initiatives.