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Salary: EUR 58,400 - 87,600 / annual
GoCardless, a Mollie company, is a global leader in bank payments processing over $130bn annually across 30+ countries. The company provides an end-to-end payment platform handling recurring and one-off payments via Direct Debit, real-time payments, and open banking, with AI-powered solutions for payment success and fraud reduction.
You'll join the Data and Business Systems group, working collaboratively with technical and non-technical stakeholders across the organization. The data engineering team works closely with analytic engineers, analysts, and business teams to deliver scalable data solutions. The team uses Python, Google Cloud Platform, dbt, Airflow, BigQuery, and other modern technologies.
Key responsibilities include:
- Design, develop, and iterate data models and schemas; build ETL/ELT pipelines to process large volumes of data efficiently and reliably
- Implement and maintain scalable data architectures and pipeline orchestration (scheduling, dependencies, retries) using cloud technologies, ensuring high availability and performance
- Implement data quality checks, monitoring, and validation processes to ensure data accuracy and consistency across systems
- Work closely with engineers, data scientists, AI/ML engineers, analysts, and business teams to understand requirements and deliver solutions
- Stay current with emerging data technologies and best practices, proposing improvements to data infrastructure
- Create and maintain technical documentation and share knowledge with team members
- Reflect GoCardless values in how you work with others
The role is open across a range of experience levels, from engineers developing pipelines and models independently to those leading the design of larger, higher-scope systems.
Requirements:
- Strong proficiency in SQL with experience in relational and/or NoSQL databases
- Experience designing and maintaining data models (dimensional, normalised, or wide-table approaches) and schema design for analytics or product use cases
- Hands-on experience building and maintaining ETL/ELT pipelines and ingesting data from third-party or internal sources using modern data engineering tools (e.g., Apache Airflow, Dataflow, or similar)
- Experience with cloud data platforms, particularly Google Cloud Platform (BigQuery, CloudSQL, Dataflow, Pub/Sub) or equivalent AWS/Azure services
- Proficiency in Python or another programming language commonly used in data engineering
- Experience with version control (Git) and CI/CD practices
- Knowledge of data governance, security best practices, and data privacy regulations
- Strong communication skills with the ability to explain technical concepts to non-technical stakeholders
- Bachelor's degree in Computer Science, Engineering, or related field, or equivalent practical experience
Nice to have:
- Experience in fintech or payments industry
- Familiarity with infrastructure as code (Terraform, CloudFormation)
- Experience designing or configuring data orchestration platforms and workflow management systems beyond day-to-day pipeline scheduling
- Familiarity with data streaming technologies and real-time data processing
- Knowledge of machine learning pipelines and supporting ML workflows
- Experience with data visualization tools and business intelligence platforms
