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Salary: EUR 49,600 - 74,400 / annual
GoCardless, a Mollie company, is a global leader in bank payments processing over $130bn annually across 30+ countries. The company serves 100,000+ businesses with Direct Debit, real-time payments, and open banking solutions.
You'll join the Data and Business Systems group, working collaboratively with technical and non-technical stakeholders across the organization. The data engineering team is large with extensive scope, and the company expects members to work with initiative and drive, holding each other accountable to high standards.
In this role, you'll help build and maintain robust, scalable, and efficient data infrastructure—from ingesting third-party data sources to developing pipelines, orchestration systems, and data models that shape insights. Working closely with analytic engineers, analysts, and business stakeholders, you'll transform raw data into valuable insights driving business decisions.
Key responsibilities include:
- Designing, developing, and iterating data models and schemas; building ETL/ELT pipelines to process large data volumes efficiently and reliably
- Implementing and maintaining scalable data architectures and pipeline orchestration (scheduling, dependencies, retries) using cloud technologies, ensuring high availability and performance
- Implementing data quality checks, monitoring, and validation processes to ensure data accuracy and consistency
- Collaborating with engineers, data scientists, AI/ML engineers, analysts, and business teams to understand requirements and deliver solutions
- Staying current with emerging data technologies and proposing improvements to infrastructure and processes
- Creating and maintaining technical documentation and sharing knowledge with team members
The company uses Python, Google Cloud Platform, dbt, Airflow, BigQuery, and other technologies. You're not expected to have expertise in all tools—most team members pick them up after joining.
The company is hiring across experience levels, from engineers developing pipelines independently to those leading design of larger, higher-scope systems.
Technologies: Python, Google Cloud Platform (BigQuery, CloudSQL, Dataflow, Pub/Sub), dbt, Airflow, and others.
Benefits include wellbeing support and medical cover, work-away scheme (up to 90 days annually), adaptive/flexible working, parental leave, annual learning budget, generous holiday allowance plus 3 volunteer days and 4 wellness days annually.
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 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
