Description & Requirements
The Role: Transform our clients through cutting edge Digital Solutions
Join our forward-thinking digital organisation, helping to build and run a trusted, resilient, and high-performing data platform that underpins operational excellence across Maximus UK. This role is responsible for improving the reliability, observability, quality, and operational supportability of our data estate, ensuring that critical data products, pipelines, and platforms are dependable, well-governed, and fit to support business, contractual, and service objectives.
Working across engineering, architecture, delivery, product, and operational teams, you will focus on making data platforms and pipelines easier to monitor, support, recover, and continuously improve, with a primary focus on our Azure Databricks-based data platform and the wider Azure data ecosystem, including Purview. You will help establish engineering standards, reliability controls, and proactive monitoring approaches that reduce incidents, improve trust in data, and enable teams to detect and resolve issues before they impact users, operations, or client outcomes. The core platform is Azure, with growing expansion into AWS, so the role will contribute to patterns and practices that support a secure and scalable multi-cloud data environment.
You will bring a strong engineering mindset to data operations, combining data platform knowledge with an understanding of resilience, automation, service health, and supportability. You will work closely with data engineers, platform teams, security, and service stakeholders to strengthen the operational maturity of the data estate, embedding a culture of quality, transparency, accountability, and continuous improvement across the end-to-end data lifecycle.
Key Responsibilities:
• Own and improve the reliability, availability, observability, and operational supportability of Maximus UK’s Azure Databricks platform, Azure data services, and associated data pipelines and data products.
• Design and implement monitoring, alerting, health checks, and diagnostics across Azure Databricks, Azure data services, orchestration layers, storage, and downstream consumption, extending these patterns into AWS as the estate grows.
• Define and maintain reliability standards, controls, operational runbooks, and support models that improve the resilience, predictability, and supportability of data services.
• Work closely with data engineering teams to identify, prioritise, and remediate reliability, performance, and data quality issues across Databricks notebooks, jobs, workflows, and other Azure data workloads.
• Establish proactive incident detection, triage, and root cause analysis practices, reducing mean time to detect and mean time to recover for data-related issues.
• Design and implement robust data quality controls, validation frameworks, reconciliation processes, and anomaly detection approaches across the end-to-end data lifecycle.
• Configure and use Azure Purview to provide effective data cataloguing, lineage, ownership, and governance, ensuring reliability and quality controls are visible and auditable.
• Collaborate with platform, cloud, architecture, and security teams to ensure the data estate is secure, resilient, cost-effective, and aligned to enterprise standards and patterns.
• Contribute to the reliability engineering approach for an Azure-first data platform while supporting reusable patterns and operational readiness for data services in AWS.
• Partner with architects and engineers so that new pipelines, data products, and platform services are designed with operability, recoverability, scalability, and observability built in from the start.
• Automate repetitive operational tasks, environment checks, dependency verification, failure handling, and recovery processes to increase efficiency and reduce manual intervention and risk.
• Capture lessons learned, codify reliability patterns and standards, and share best practice to continuously improve reliability, transparency, and engineering discipline across the data function.