Senior Data Engineer

Key Responsibilities:

• Design, build, and maintain CI/CD pipelines using Azure DevOps for automated testing, container image builds, and deployment
• Containerise the data quality platform using Docker and manage deployment of container images across Business environments using Kubernetes
• Spin up and configure new container instances when onboarding new Business areas onto the platform
• Develop and maintain automated testing frameworks covering unit, integration, and end-to-end testing
• Design, build, and expose RESTful APIs to integrate the platform with upstream and downstream systems
• Collaborate with the Data Architecture team to ensure platform components are modular, platform-agnostic, and deployable across cloud and on-premise environments
• Work with Azure services including Azure Container Registry, Azure Kubernetes Service, Azure Key Vault, Azure Blob Storage, and Azure DevOps
• Develop and optimise data pipelines using PySpark and SQL Server
• Support the integration of Databricks workloads into the platform architecture
• Ensure platform security standards are met including secret management, image vulnerability scanning, and access controls
• Contribute to technical documentation, runbooks, and onboarding guides for Business teams

Required Skills & Experience:

• 8–12 years of experience in data engineering or platform engineering roles
• Strong hands-on experience with Azure services — AKS, ACR, Key Vault, Blob Storage, Azure DevOps
• Proven experience containerising applications using Docker and orchestrating with Kubernetes
• Solid experience building and maintaining CI/CD pipelines in Azure DevOps
• Experience designing and building RESTful APIs
• Proficiency in PySpark and SQL Server
• Experience with Databricks — job orchestration, cluster management, notebook development
• Strong understanding of software testing practices — unit testing, integration testing, end-to-end testing
• Experience with branching strategies, versioning, and release management
• Strong problem-solving skills and ability to work independently in a fast-paced environment

Preferred Skills:

• Understanding of Data Quality concepts — profiling, validation rules, DQ scoring, observability
• Experience with data quality tooling such as Informatica, Great Expectations, or similar
• Familiarity with Flask or similar Python web frameworks
• Experience with modular, microservice-style architecture design

What We're Looking For:

Beyond technical skills, we are looking for someone who takes ownership, thinks like a platform engineer, and understands that the best infrastructure is the kind that Business teams never have to think about. The ideal candidate is equally comfortable writing a Dockerfile, debugging a failing pipeline, and discussing deployment governance with a non-technical stakeholder.