KEY INFORMATION
Position: Data Engineer
Reporting to: Data Engineering Manager
Location: Manchester Office, Hybrid working schedule
Role Overview
As a Data Engineer, you will help shape the evolution of CMSPI’s payments intelligence platform. You will work across the full data engineering lifecycle, designing secure data retrieval and ingestion capabilities, building reliable analytics workflows and supporting client onboarding. Working closely with a cross-functional development squad, you will turn business requirements into maintainable solutions while learning and applying modern engineering practices and technologies.
Requirements
JOB ROLE
Key accountability for this role: Deliver innovative, scalable and secure data solutions for CMSPI’s payments intelligence platform.
Key responsibilities will include:
• Design scalable data architectures and retrieval solutions across APIs, web scrapers and SFTP, with security and performance at the centre.
• Deliver prompt, efficient onboarding of CMSPI client data and contribute to new data features that create continuous business value.
• Collaborate with engineers, agile delivery leads and business stakeholders to define, refine and deliver requirements in an agile squad.
• Maintain high software quality through clean code, collaborative code and architecture reviews, automated testing and continuous integration.
• Respond to stakeholder issues and queries, troubleshoot production problems and drive practical improvements to data solutions.
• Help shape CMSPI’s next generation data platform using powerful tools such as Databricks AI Gateway, enabling governed, agent-powered capabilities that support self-building and self-healing data pipelines.
• Participate in sprint planning and daily stand-ups, keeping stories and tasks current and raising critical blockers early.
• Learn, adopt and promote best practices in data engineering, including reusable Python libraries and maintainable functional and object-oriented code.
• Stay aware of emerging technology trends and research ideas that can improve CMSPI’s data platforms and engineering practices.
• Work collaboratively as part of an Agile development squad and contribute to a culture of continuous learning and delivery.
WHAT WE’RE LOOKING FOR
You are a great match if:
• 2+ years’ professional experience as a Data Engineer or DevOps Engineer.
• Experience building ETL/ELT workflows for analytics using Spark or Hadoop.
• Strong Microsoft Azure experience across DevOps/CI/CD, Azure Data Factory, Azure Data Lake, Azure Functions, Key Vault, monitoring, networking, identity, and cloud deployment services.
• Databricks experience, including workflows/jobs, notebooks, Delta Lake, Unity Catalog and other Databricks-native tools.
• Advanced Python skills, including efficient functional and object-oriented code, reusable Python libraries, and practical experience with packages such as requests, Selenium, and Playwright.
• Strong problem-solving, analytical, communication and cross-functional collaboration skills.
• Bachelor’s degree in Computer Science, Mathematics or another analytical/numerical subject.
• Highly desirable: experience with Agile squads, payments, Terraform/Bicep, Docker/Kubernetes, Scala, PowerShell or YAML.
Benefits
What we offer…
• Competitive annual company bonus linked to company and individual performance.
• Future opportunity for equity (shadow stock) awarded to high performers.
• Competitive pension plan from day 1 plus Vitality healthcare cover & cycle to work (after 6-months)
• Monthly reimbursement for commuting costs.
• 22 days' annual leave, increasing with service, plus UK bank holidays.
• Wellbeing support, including an Employee Assistance Programme and mental health resources.
• Learn from payments industry experts and accelerate your career through ongoing training and development.
• Personal and professional growth opportunities, with personalised development plans tailored to your ambitions.
• Quarterly employee recognition awards including a financial prize.
• Regular officewide and team-based socials.
