Data Engineer

GoCardless Ltd; Lisboa; Sur place; EUR 65 000 - 90 000; Plein temps

Résumé du poste

GoCardless Ltd in Lisbon, Portugal, is seeking a Data Engineer to join our Data and Business Systems team. You’ll build scalable data infrastructure, ingest third-party data, and develop ETL/ELT pipelines to enable analytics across the business.

You’ll collaborate with analytical engineers, analysts, and stakeholders to transform raw data into actionable insights, with a focus on reliability, performance, and governance in a fintech environment.

Qualifications

• Strong proficiency in SQL for analytics and data access.
• Experience designing data models (dimensional/normalised) for analytics or product use cases.
• Hands-on experience building ETL/ELT pipelines and ingesting data from third-party or internal sources using modern tools.

Responsabilités

• Design, develop, and maintain scalable data models and pipelines to process large data volumes.
• Implement and manage data infrastructure with cloud technologies ensuring high availability and performance.
• Collaborate with engineers, analysts, and stakeholders to deliver data solutions that drive business decisions.

Connaissances

SQL proficiency

Data modelling

ETL/ELT pipelines

Cloud data platforms

Team collaboration

Outils

Apache Airflow

Dataflow

Description du poste

About us

GoCardless, a Mollie company, is a global leader in bank payments. Over 100,000 businesses, from start-ups to household names, use GoCardless to collect, manage and send bank payments through Direct Debit, real-time payments and open banking. With US$130bn+ processed annually across 30+ countries, we handle recurring and one-off payments without the chasing, stress, or expensive fees. Our end-to-end payment platform also features AI-powered solutions to improve payment success and reduce fraud, alongside connections to over 350 platforms businesses use everyday.

We are headquartered in the UK, with teams and operations spanning North America, Europe and Asia-Pacific. For more information, please visit www.gocardless.com and follow us on LinkedIn @GoCardless.

Mollie is the leading payments and financial services partner for business, rooted in Europe, with global reach.

Data at GoCardless

You’ll sit in our Data and Business Systems group, working with technical and non-technical people across the whole company. You’ll be part of a collaborative data engineering team, working closely with analytic engineers, analysts, and business stakeholders to deliver data solutions that scale with our rapidly growing business.

We’re a large team with an extensive remit, and we expect every member to work with initiative and be driven - holding each other accountable and to a high standard. Joining our team, you’ll work alongside people who strive to be the best they can be, on complex projects across GC's data and business systems stack.

Our technologies: We endeavour to build simple, reliable systems, and we believe in using the best technology for each task. Technologies we use across GoCardless include Python, Google Cloud Platform, dbt, Airflow, BigQuery, and others.

You’re not expected to have expertise in all of these - most of our team have picked up tools once they’ve started working with us.

The Role

We are looking for a talented Data Engineer to help us build and maintain robust, scalable, and efficient data infrastructure - from ingesting data from third-party sources, to the pipelines and orchestration that move our data, to the models that shape it.

Working closely with analytic engineers, analysts, and business stakeholders, you’ll help transform raw data into valuable insights that drive business decisions.

We’re hiring across a range of experience levels - from engineers developing pipelines and models independently, through to those leading the design of larger, higher-scope systems.

The main elements of this role will involve:

• Data modelling and pipeline development: Design, develop, and iterate data models and schemas, and build ETL/ELT pipelines to process large volumes of data efficiently and reliably.
• Data infrastructure development: Implement and maintain scalable data architectures and pipeline orchestration (scheduling, dependencies, retries) using cloud technologies, ensuring high availability and performance.
• Data quality and governance: Implement data quality checks, monitoring, and validation processes to ensure data accuracy and consistency across systems.
• Collaboration and delivery: Work closely with engineers, data scientists, AI/ML engineers, analysts, and business teams across the company to understand requirements and deliver data solutions that meet their needs.
• Technical innovation: Stay current with emerging data technologies and best practices, proposing and implementing improvements to our data infrastructure and processes.
• Documentation and knowledge sharing: Create and maintain technical documentation, share knowledge with team members, and contribute to engineering best practices.

Be a reflection of the GC Values that help us work with one another effectively.

Who we’re looking for

You’ll enjoy this role if:

• You’re passionate about working with data at scale and solving complex data challenges - whether that’s shaping how data is modelled, how it moves, or how reliably it’s delivered.
• You write clean, maintainable, and efficient code, and care about getting the details right.
• You enjoy collaborating across diverse teams, and can translate business requirements into technical solutions.
• You think critically and solve problems methodically - debugging complex data issues and optimising system performance.
• You take ownership of your work, proactively looking for ways to improve it.
• You’re eager to learn and grow in a fast-paced fintech environment.

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