Senior Software Engineer - Data Engineer

Remote; San Francisco (CA); Remote; USD 101,000 - 168,000; Full time

Benefits offered by this job

Fully remote work

International engineering team

Professional development

Flexible working hours

Home office equipment allowance

Collaborative startup-style culture

Job summary

Remote is seeking a Senior Data Engineer to design, build, and maintain scalable data infrastructure powering analytics and reporting. You will own end-to-end ETL/ELT pipelines, transform raw data into reliable datasets, and work with cloud platforms and modern data warehousing technologies.

You will collaborate with international teams, improve data quality, observability, and cost efficiency, and contribute to robust production systems in a fully remote environment.

Qualifications

• Four+ years of professional data engineering experience delivering production-ready data solutions.
• Strong SQL skills for querying, transforming, and optimizing large datasets.
• Proficiency in Python, Scala, or Java for data processing.
• Hands-on ETL/ELT experience in production environments.
• Experience with at least one cloud data platform (AWS, GCP, or Azure).
• Experience with modern data warehousing technologies.
• Solid data modeling, schema design, and data quality understanding.
• Ability to diagnose data issues and fix production systems.
• Knowledge of building scalable and cost-efficient data infrastructure.
• Strong communication and collaboration in international teams.
• Proactive ownership and autonomy in a remote-first setup.

Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines ingesting data from diverse sources.
• Develop and optimize data models, schemas, and transformation logic for analytics and BI.
• Establish data validation, quality checks, and reliability standards.
• Monitor pipeline performance, troubleshoot data issues, and implement solutions.
• Implement observability, monitoring, and alerting for production data systems.
• Build and maintain cloud-based data infrastructure and warehousing solutions.
• Identify opportunities to improve processing speed, resource use, and costs.
• Collaborate with analytics, product, data science, and engineering teams to meet data needs.
• Contribute to coding standards, testing, deployment practices, and workflows.
• Create thorough documentation for pipelines, models, and infrastructure.
• Participate in on-call rotations and incident response as needed.
• Continuously evaluate new technologies to strengthen the data platform.

Skills

SQL

Python/Scala/Java

ETL/ELT pipelines

Cloud platforms

Data warehousing

Data modeling

Data quality

Troubleshooting

Scalability

Communication

Ownership

Job description

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior Software Engineer – Data Engineer based in Netherlands.

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.

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.