# Data Engineer III

[Gocardless](https://gurify.com/jobs?q=Gocardless) · Lisbon, Portugal · Posted yesterday

[Data](https://gurify.com/jobs/data)

[Apply on the original posting → (opens in a new tab)](https://job-boards.greenhouse.io/gocardless/jobs/8210486)

## Job description

### 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 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 the 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.

### Base salary range: €58,400 - €87,600

Base salary ranges are based on role, job level, location, and market data. Please note that whilst we strive to offer competitive compensation, our approach is to pay between the minimum and the mid-point of the pay range until performance can be assessed in role. Offers will take into account level of experience, interview assessment, budgets and parity between you and fellow employees at GoCardless doing similar work.

### (some of) The good stuff

- Wellbeing - stay healthy with dedicated support and medical cover

- Work away scheme - gives you the option to work away from your country of residence for up to 90 days in any 12 month period

- Adaptive Working - allows you to work flexibly, around your lifestyle

- Parental leave - to suit everyone embarking on life's great adventure

- Learning Budget - lead your own development with an annual learning budget

- Time off - generous holiday allowance, + 3 annual volunteer days, + 4 annual business-wide wellness days (‘GC Fridays’)

### Life at GoCardless

We're an organisation defined by our values; We start with why before we begin any project, to ensure it’s aligned with our mission. We act with integrity, always. We care deeply about what we do and we know it's essential that we be humble whilst we do it. Working this way creates the GC magic- the reason we all love showing up to work.

### Diversity & Inclusion

As of April 2025, we had 806 employees (GeeCees) globally, with 524 based in the UK, 163 based in Latvia and 119 across our other offices.

To ensure that we're representative of the world around us - and to be able to review relevant benchmarks - we ask GeeCees to voluntarily disclose diversity data. This year, the proportion of GeeCees providing data increased to 88% (up from 79% in 2024). With regards to diversity within GoCardless, we can see GeeCees identifying as:

### Asian, Black, Mixed or Other — 25%

### Neurodiverse — 9%

### LGBTQIA+ — 9%

### Disabled — 1%

### Average age — 33

### Female — 45%

### Male — 55%

We’re rooting for you during your application and GoCardless aims to provide reasonable adjustments to make our recruitment process as remarkable and accessible as we can. Please speak to your Talent Partner if you need extra support.

If you want to learn more, you can read about our Employee Resource Groups and objectives here

### Sustainability

We’re committed to reducing our impact on the environment, leaving a more sustainable world for future generations. Check out our sustainability action plan here.

Find out more about Life at GoCardless via Twitter, Instagram and LinkedIn.

**Live in Gocardless’s hiring system.** Read from the company's own applicant tracking system, not reposted from a job board — so it's a real, open requisition rather than an ad that outlived the role.

We remove it as soon as it disappears at source.

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whether that\u0026#39;s shaping how data is modelled, how it moves, or how reliably it\u0026#39;s delivered.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EYou write clean, maintainable, and efficient code, and care about getting the details right.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EYou enjoy collaborating across diverse teams, and can translate business requirements into technical solutions.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EYou think critically and solve problems methodically - debugging complex data issues and optimising system performance.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EYou take ownership of your work, proactively looking for ways to improve it.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EYou\u0026#39;re eager to learn and grow in a fast-paced fintech environment.\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003ERequirements\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EStrong proficiency in SQL, with experience in relational and/or NoSQL databases.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience designing and maintaining data models (dimensional, normalised, or wide-table approaches) and schema design for analytics or product use cases.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EHands-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).\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience with cloud data platforms, particularly Google Cloud Platform (BigQuery, CloudSQL, Dataflow, Pub/Sub) or equivalent AWS/Azure services.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EProficiency in Python or another programming language commonly used in data engineering.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience with version control (Git) and CI/CD practices.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EKnowledge of data governance, security best practices, and data privacy regulations.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EStrong communication skills, with the ability to explain technical concepts to non-technical stakeholders.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EBachelor\u0026#39;s degree in Computer Science, Engineering, or related field, or equivalent practical experience.\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003ENice to have\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EExperience in the fintech or payments industry.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EFamiliarity with infrastructure as code (Terraform, CloudFormation).\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience designing or configuring data orchestration platforms and workflow management systems, beyond day-to-day pipeline scheduling.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EFamiliarity with data streaming technologies and real-time data processing.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EKnowledge of machine learning pipelines and supporting ML workflows.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience with data visualization tools and business intelligence platforms.\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EBase salary range: \u20AC58,400 - 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We act with integrity, always. We care deeply about what we do and we know it\u0026#39;s essential that we be humble whilst we do it. Working this way creates the GC magic- the reason we all love showing up to work.\u003C/p\u003E\u003Ch3\u003EDiversity \u0026amp; Inclusion\u003C/h3\u003E\u003Cp\u003EAs of April 2025, we had 806 employees (GeeCees) globally, with 524 based in the UK, 163 based in Latvia and 119 across our other offices.\u003C/p\u003E\u003Cp\u003ETo ensure that we\u0026#39;re representative of the world around us - and to be able to review relevant benchmarks - we ask GeeCees to voluntarily disclose diversity data. This year, the proportion of GeeCees providing data increased to 88% (up from 79% in 2024). 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