Data Engineer
- Design, build, and maintain scalable and resilient data pipelines and infrastructure, utilising Python for custom data transformations, API integrations, and orchestration.
- Implement and manage data platforms leveraging Kubernetes for efficient deployment, orchestration, and scaling of data services and applications.
- Own the flow of data through our Snowflake data warehouse and its component parts, ensuring optimal data delivery architecture and data availability for global business operations.
- Develop and implement internal process improvements, including CI/CD pipelines using Azure DevOps and GitHub for automated testing, deployment, and version control.
- Collaborate closely with stakeholders across the business, including Engineering, Data Scientists and Analytics Engineering teams, to gather requirements and deliver high-impact data solutions.
- Ensure data quality, reliability, and observability across our data platform, implementing robust monitoring, alerting, and testing frameworks.
- Solve complex technical problems in ambiguous situations, demonstrating a strong ability to identify root causes and implement innovative, efficient solutions to intricate data challenges.
- Maintain our data infrastructure for efficiency and consistency across multiple regions.
- Proven experience as a Data Engineer, designing, building, and maintaining scalable data platforms and pipelines.
- Strong Python skills for data engineering, scripting, and automation.
- Proficient in SQL and dbt, with a solid understanding of data warehousing concepts.
- Experience with cloud data warehouses (e.g., Snowflake) and ELT tools (e.g., Fivetran).
- Demonstrated experience with Azure DevOps and/or GitHub, CI/CD, and collaborative development.
- Exceptional problem-solving skills, capable of navigating and resolving complex technical challenges in ambiguous, fast-paced environments.
- Strong stakeholder management and communication skills, comfortable working with technical and non-technical colleagues.
- A strong focus on data quality, reliability, testing, and performance.
- Track record of delivering clean, well-documented, and production-grade data assets and infrastructure.
- Strategic mindset with experience contributing to and delivering against a team roadmap, balancing business priorities with technical improvements and managing technical debt.
- Experience with Kubernetes for deploying data services.
Worldwide travel insurance through Vitality
Anniversary Rewards (£250, £500, £750, 4-week fully paid sabbatical)
Salary Sacrifice Pension Scheme up to 7% match
28 days holiday (plus bank holidays)
Annual Learning and Wellbeing Budget
Enhanced Parental Leave
Cycle to Work Scheme
Season Ticket Loan
6 free therapy sessions per year
Dog Friendly Offices
Free drinks and snacks in our offices Check out more of our benefits, values and mission here.
Interview Process
First stage: 30 minute intro and values call with Talent Partner (Video call)
Second stage: 45 minute CV overview with Team Manager (Video call)
Final stage: 75 minute technical assessment with members of the Data Engineering team (Video call) Other Info Check out our 'Top Tips' for interviewing.
• Keep updated on new job opportunities by following us on Linkedin.
📧Email [email protected] if you have any questions. Excited to work here? Apply! If you'd like to progress your career within our fast growing, profitable fintech then click apply and we will aim to get back to you within 3 working days (during busy periods this could take up to 5 working days.)
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