Data Reliability Engineer

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. Essential Skills - What You'll Bring • Proven experience in data engineering, platform engineering, site reliability engineering, DataOps, or a closely related role focused on data platform reliability and operations. • Strong hands-on experience with Azure-based data platforms, particularly Azure Databricks and core Azure data services such as Data Lake Storage, Data Factory/Synapse, and analytical stores, with familiarity of equivalent services in AWS. • Strong understanding of modern data platform architectures, including data lakes, warehouses or lakehouses, orchestration frameworks, transformation pipelines, streaming services, and analytical consumption layers. • Experience designing and implementing monitoring, observability, logging, alerting, and incident management approaches for Databricks workloads and Azure data services, using tools such as Azure Monitor, Log Analytics, or similar. • Strong understanding of data quality, reconciliation, validation, and lineage concepts, and practical experience implementing control frameworks that protect critical data flows and products. • Hands-on experience with Azure Purview or comparable data cataloge and lineage tooling, including configuration of collections, classification, ownership, and lineage for key datasets. • Good understanding of reliability engineering principles such as availability targets, resilience patterns, recoverability, service health indicators, and operational readiness assessments. • Experience using scripting and automation (for example, Python, PowerShell, or similar) to remove operational toil, improve repeatability, and strengthen recovery and deployment processes. • Ability to diagnose and resolve complex issues that span data pipelines, integrations, cloud infrastructure, configuration, and source or downstream systems, and to drive pragmatic remediation. • Strong collaboration and communication skills, with the ability to work effectively across data engineering, architecture, cloud platform, security, delivery, and operational teams, and to explain issues and trade-offs clearly to technical and non-technical stakeholders. • Experience with Azure-native observability tooling and patterns for Databricks and the wider Azure data stack, and exposure to equivalent AWS monitoring approaches. Desirable Skills - What Makes You Stand Out • Experience tuning and optimising Databricks jobs, clusters, and workflows for performance, cost efficiency, and reliability in production environments. • Familiarity with CI/CD practices, infrastructure as code, and automated testing for data platforms, supporting safe, repeatable, and low-risk changes to data services. • Experience working in regulated or contractually governed environments such as public sector, health, employment services, or similarly controlled industries, where service stability and compliance are critical. • Experience contributing to platform standards, engineering communities of practice, or cross-team reliability improvement initiatives, helping to raise engineering and operational maturity across multiple squads or domains. EEO Statement • MAXIMUS is committed to developing, maintaining and supporting a culture of diversity, equity and inclusion throughout the recruitment process.
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• Where reasonable, MAXIMUS will review and consider adjustments for those applicants who express a requirement for them during the recruitment process. Minimum Salary £ 56,500.00 Maximum Salary £ 65,000.00 *