• Data Pipeline Development: Design, build, and maintain scalable ETL/ELT pipelines that ingest, transform, and deliver data from diverse internal and external sources.
• Data Modeling and Architecture: Develop and optimize data models, schemas, and transformation logic to support analytics, reporting, business intelligence, and downstream application requirements.
• Data Quality and Reliability: Establish and maintain data validation processes, quality checks, and reliability standards to ensure datasets remain accurate, consistent, and trustworthy.
• Pipeline Monitoring and Troubleshooting: Monitor pipeline performance and health, independently investigate data issues, identify root causes, and implement effective solutions to minimize disruptions.
• Observability and Alerting: Implement monitoring, alerting, and observability practices that improve visibility into production data systems and enable proactive incident detection and resolution.
• Cloud Data Infrastructure: Build and maintain data solutions using cloud platforms and modern data warehousing technologies, ensuring systems remain scalable, secure, and efficient.
• Performance and Cost Optimization: Identify opportunities to improve processing speed, resource utilization, scalability, and the cost-effectiveness of existing data infrastructure.
• Cross-Functional Collaboration: Partner with analytics, product, data science, and engineering teams to understand their data requirements and translate them into durable, well-designed technical solutions.
• Testing and Engineering Standards: Contribute to team standards for code quality, automated testing, code reviews, deployment practices, and maintainable data engineering workflows.
• Technical Documentation: Create clear and comprehensive documentation covering data pipelines, models, schemas, infrastructure, and operational procedures to support knowledge sharing and maintainability.
• Production Support: Participate in on-call rotations and incident response activities as needed, helping ensure the stability, availability, and reliability of production data systems.
• Continuous Improvement: Evaluate new technologies, tools, and engineering practices that can strengthen the data platform and improve the effectiveness of the wider engineering organization.

Requirements:

• Professional Experience: At least four years of professional experience in data engineering or a closely related technical discipline, with a strong track record of delivering production-ready data solutions.
• SQL Expertise: Strong SQL skills, including the ability to query, transform, analyze, and optimize complex datasets efficiently.
• Programming Skills: Proficiency in at least one programming language commonly used in data engineering, such as Python, Scala, or Java.
• ETL/ELT Pipeline Experience: Hands-on experience designing, implementing, operating, and maintaining ETL/ELT pipelines in production environments.
• Cloud Platform Knowledge: Practical experience with at least one major cloud data platform, such as AWS, Google Cloud Platform (GCP), or Microsoft Azure.
• Modern Data Warehousing: Experience working with contemporary data warehousing technologies and designing solutions that support reliable data access and downstream analytics.
• Data Modeling and Quality: Solid understanding of data modeling principles, schema design, data quality management, and pipeline orchestration.
• Troubleshooting and Problem-Solving: Ability to work independently with large, complex datasets, diagnose data issues, and implement effective solutions in production systems.
• Scalability and Reliability: Understanding of the engineering principles required to build robust, maintainable, and cost-efficient data infrastructure.
• Communication and Collaboration: Ability to communicate technical concepts clearly, document solutions effectively, and collaborate with multidisciplinary teams in an international, distributed environment.
• Ownership and Adaptability: A proactive approach to problem-solving, a strong sense of responsibility for production systems, and the ability to work autonomously in a remote-first environment.

Nice to have:

• Experience with workflow orchestration tools such as Apache Airflow, Dagster, or similar technologies.
• Familiarity with streaming data technologies, including Apache Kafka or Amazon Kinesis.
• Experience with dbt or comparable data transformation frameworks.
• Exposure to infrastructure-as-code practices and tools such as Terraform.
• Previous experience working directly with analytics or data science teams to support reporting, experimentation, and advanced analytical use cases.
• Experience contributing to engineering standards, improving development workflows, and supporting production systems at scale.

Benefits:

• Competitive Compensation: Salary-based compensation complemented by benefits.
• Paid Time Off: Paid vacation and sick leave to support personal well-being and work-life balance.
• Fully Remote Work: Work remotely from Europe.
• Flexible Working Hours: Organize your working schedule flexibly, with an emphasis on delivering high-quality results rather than adhering to rigid working hours.
• Home Office Equipment Allowance: Receive a generous allowance to equip your home workspace for productivity and comfort.
• Coworking Options: Access a desk at a nearby office or coworking facility, with the flexibility to work from home, use a coworking space, or combine both options.
• No Business Travel Required: Enjoy a fully remote working arrangement without mandatory business travel.
• International Engineering Team: Collaborate with highly skilled software engineering specialists on challenging projects for major enterprise clients.
• Professional Development: Access opportunities to expand your technical expertise, work with modern technologies, and grow professionally.
• Collaborative Work Environment: Join a dynamic, startup-style culture that encourages innovation, knowledge sharing, and practical problem-solving.
• Technical Ownership and Impact: Take responsibility for meaningful data engineering initiatives that support analytics, reporting, and product capabilities.