Data Engineer- UK

Coreviewmid

In short

▸Ingeniero de datos que construye y gestiona pipelines de uso y adopción en Databricks para métricas empresariales.
▸Se enfoca en automatización, calidad de datos y acceso controlado a métricas con seguridad a nivel de fila.
▸Destacado: uso obligatorio de herramientas de IA asistida (Cursor, Claude) en el flujo diario de trabajo.

Fluent English required for collaboration across global teams

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What they ask for

✓3 a 5 años construyendo pipelines de datos en entorno comercial.
✓Experiencia práctica con Databricks, Unity Catalog y Spark Declarative Pipelines.
✓Conocimiento avanzado de SQL y Python para ETL/ELT.
✓Capacidad para gestionar cambios en esquemas y datos inconsistentes.
✓Experiencia con CI/CD y despliegues reproducibles en producción.
✓Habilidad para colaborar con producto y post-venta en definición de métricas clave.

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DatabricksUnity CatalogSpark Declarative PipelinesSQLPythonPower BICursorClaudeCI/CDAI-assisted development

Who should you write to at Coreview?

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About CoreView CoreView is the global leader in Microsoft 365 (M365) tenant resilience, serving over 23 million users worldwide. We empower the world’s leading organizations to master the complexity of Microsoft M365. Through robust security and precise governance, we help ensure that our client’s environments stay cyber-resilient and productive, no matter how complex they are. Our unified, cloud-native platform delivers powerful automation, rapid value, and end-to-end visibility across the entire M365 ecosystem. Backed by world-class support and a collaborative, innovative culture, CoreView is a place where your ideas matter, and your work truly impacts global enterprises. Job Summary This role sits in Operations. You'll report to the Data Analyst Manager helping establish our data practice, including documented pipelines, governed metric definitions, and an established way for other departments to ask for data without it becoming ad hoc, bespoke work every time. In addition, CoreView is improving how we understand product telemetry, platform product usage, licence utilisation and adoption trends. You will work across business functions and take ownership of CoreView's customer usage and adoption analytics and turn them into a production-grade, reliable system, then help scope and build the next set of metrics and signals on top of them. You'll work primarily in Databricks (Unity Catalog, Spark Declarative Pipelines), using SQL and Python day to day, and you'll be expected to use AI-assisted development (Cursor, Claude, or equivalent) as a normal part of how you work. Job Responsibilities • Take operational ownership of the usage and adoption pipelines and their alerting • Reconcile figures derived from our product-usage tool • Separate development and production environments and mature CI/CD so deployment is repeatable and rollback is reliable. • Build and maintain Bronze-to-Gold pipelines in Databricks, Unity Catalog metric views, and the permissions/row-level-security model behind them. • Extend automated data quality checks across the underlying gold-layer tables and validate outputs against existing Power BI reports. • Co-own the scoping of the next set of usage and adoption signals with Product and Post-Sales: prioritising upsell signals and the ROI narrative and confirming what needs new instrumentation. • Handle the realities of business system data: schema drift, inconsistent field naming, soft deletes, incremental loads • Lead and be responsible for the consolidation of additional data sources — including support tickets, audit logs, CRM, and other customer-facing systems — while defining the feasibility, effort, cost, and implementation approach required to support a future unified data platform. • Establish documented pipelines, an access model for raw and transformed data, and a working intake process for cross-department data requests. • Act as a credible point of contact for CoreView's Databricks estate • Bring automation and AI-assisted tooling into the day-to-day work — for example automated monitoring and anomaly detection on metric movements — as the platform matures. Job Requirements • 3 to 5 years' professional experience building and operating production data pipelines (ETL/ELT) in a commercial setting. • Hands-on Databricks proficiency: Unity Catalog, Spark Declarative Pipelines and metric views, including the permissions / row-level-security model behind them. • Strong SQL and Python applied to

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