Senior Data Engineer Riga, Latvia

GoCardless is a global bank payment company. Over 100,000 businesses
, from start-ups to household names, use GoCardless to collect and send payments through direct debit, real-time payments and open banking.

GoCardless processes US $130bn+ of payments annually, across 30+ countries
; helping customers collect and send both recurring and one-off payments
, without the chasing, stress or expensive fees. We use AI-powered solutions to improve payment success and reduce fraud. And, with open banking connectivity to over 2,500 banks
, we help our customers make faster, more informed decisions.

We are headquartered in the UK with offices in London and Leeds
, and additional locations in Australia, France, Ireland, Latvia, Portugal and the United States.

At GoCardless, we're all about supporting you
! We’re committed to making our hiring process inclusive and accessible
. If you need extra support or adjustments, reach out to your Talent Partner — we’re here to help!

And remember: we don’t expect you to meet every single requirement. If you’re excited by this role,
we encourage you to apply!

We are looking for a talented Senior Data Engineer to join our team in building and maintaining robust, scalable, and efficient data infrastructure. You will be responsible for designing and implementing data pipelines, optimising data systems, and ensuring high-quality data delivery across the organisation. Working closely with analytic engineers, analysts, and business stakeholders, you'll help transform raw data into valuable insights that drive business decisions.

You'll sit in our Data and Business Systems group and will work with technical and non-technical people across the whole company. You'll be part of a collaborative data engineering team and will work closely with commercial and operational stakeholders to deliver data solutions that scale with our rapidly growing business.

• Building and maintaining data pipelines:
  Design, develop, and optimise ETL/ELT pipelines to process large volumes of data efficiently and reliably.
• Data infrastructure development:
  Implement and maintain scalable data architectures 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 analytic engineers, analysts, and business teams 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

• Someone passionate about working with data at scale and solving complex data challenges.
• Someone who writes clean, maintainable, and efficient code with attention to detail.
• Someone who enjoys collaborating with diverse teams and can translate business requirements into technical solutions.
• Someone with strong problem-solving skills who can debug complex data issues and optimise system performance.
• Someone who takes ownership of their work and proactively identifies opportunities for improvement.
• Someone eager to learn and grow in a fast-paced fintech environment.

• Strong proficiency in SQL and experience with relational and No
  
  SQL databases.
• Hands‑on experience building and maintaining ETL/ELT pipelines using modern data engineering tools (e.g., Apache Airflow, Dataflow, or similar).
• Experience with cloud data platforms, particularly Google Cloud Platform (Big Query, Cloud
  
  SQL, Dataflow, Pub/Sub) or equivalent AWS/Azure services.
• Proficiency in Python or another programming language commonly used in data engineering.
• Understanding of data modelling concepts, data warehousing principles, and dimensional modelling.
• Experience with version control systems…