Senior Software Engineer - Data Engineer

Accountabilities:

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.