Senior Data Engineer at GoCardless

Rīga, , Latvia -

Full Time

Start Date

Immediate

Expiry Date

17 Feb, 26

Salary

96000.0

Posted On

19 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

SQL, ETL, ELT, Data Engineering, Cloud Technologies, Python, Data Modelling, Data Warehousing, Version Control, CI/CD, Data Streaming, Data Governance, Data Privacy, Communication, Problem Solving, Collaboration

Industry

IT Services and IT Consulting

Description

About Us at GoCardless 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! The Role 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. The main elements of this role will involve: 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 Who we're looking for 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. Requirements Strong proficiency in SQL and experience with relational and NoSQL 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 (BigQuery, CloudSQL, 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 (Git) and CI/CD practices. Familiarity with data streaming technologies and real-time data processing is a plus. 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 with data orchestration tools and workflow management systems. Knowledge of machine learning pipelines and supporting ML workflows. Experience with data visualisation tools and business intelligence platforms. Salary range: EUR 64,000 - 96,000 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. Please note, this role is initially being offered through an Employer of Record (EOR), our standard benefits package will be available upon transition to direct employment. The Good Stuff! Wellbeing: Dedicated support and medical cover to keep you healthy. Work Away Scheme: Work from anywhere for up to 90 days in any 12-month period. Hybrid Working: Our hybrid model offers flexibility, with in-office days determined by your team. Equity: All permanently employed GeeCees get equity to share in our success. Parental leave: Tailored leave to support your life's great adventure. Time Off: Generous holidays, 3 volunteer days, and 4 wellness days annually. 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 make it happen, working with urgency and taking personal accountability for getting things done. 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. Our Values form part of the GoCardless DNA, and are used to not only help us nurture and develop our culture, but to deliver impactful work that will help us to achieve our vision. Diversity & Inclusion We’re building the payment network of the future, and to achieve our goal, we need a diverse team with a range of perspectives and experiences. As of July 2024, here’s where we stand: 45% identify as women 23% identify as Black, Asian, Mixed, or Other 10% identify as LGBTQIA+ 9% identify as neurodiverse 2% identify as disabled If you want to learn more, you can read about our Employee Resource Groups and objectives here as well as our latest D&I Report Sustainability at GoCardless We’re committed to reducing our environmental impact and leaving a sustainable world for future generations. As co-founders of the Tech Zero coalition, we’re working towards a climate-positive future. Check out our sustainability action plan here. Find out more about Life at GoCardless via X, Instagram and LinkedIn.

Responsibilities

The Senior Data Engineer will design and implement data pipelines, optimize data systems, and ensure high-quality data delivery across the organization. They will collaborate with analytic engineers, analysts, and business stakeholders to transform raw data into valuable insights that drive business decisions.