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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Software Engineer - Data Engineer - **Company:** Jobgether - **Location:** Netherlands (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Airflow, Amazon Web Services, Automation of Tests, Microsoft Azure, Big Data, Cloud Database, Software Quality, Code Review, Computer Programming, Data Architecture, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Transformation, Data Security, Data Systems, Data Warehousing, Database Queries, Programming Tools, Monitoring of Systems, Python (Programming Language), Operational Databases, Raw Data, Standard Sql, Data Streaming, Google Cloud, Cloud Platform System, Infrastructure Automation Frameworks, Data Analytics, Apache Kafka, Terraform, Data Pipelines, Programming Languages - **Published:** October 30, 2026 - **Apply:** https://www.adzuna.nl/details/5946011592 ## About the Role * 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. ## Description We are looking for an experienced Senior Data Engineer to design, build, and maintain scalable data infrastructure that powers analytics, reporting, and data-driven product development. In this fully remote role, you will take end-to-end ownership of ETL/ELT pipelines, transforming complex raw data into reliable, well-structured, and accessible datasets. You will work with modern cloud platforms, data warehousing technologies, and programming tools to deliver robust, high-performance data solutions. Collaborating with engineering, product, analytics, and data science teams, you will translate business requirements into dependable data architectures. You will also play a key role in improving data quality, system observability, scalability, and cost efficiency across production environments. This opportunity offers the chance to tackle challenging engineering problems alongside an international team of talented professionals, with flexible working arrangements and opportunities for continuous growth. 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.