Lead Analytics Engineer

About us

Graphcoreis one of the worlds leading innovators in Artificial Intelligencecompute. It is developing hardware software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry.

As part of the SoftBank GroupGraphcoreis a member of an elite family of companies responsible for some of the worlds most transformative technologies. Together they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone.

Graphcoresteams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists silicon designers softwareengineersand systems architectsGraphcorebrings together deepexpertiseto solve complex problems and deliver meaningful progress in AIcompute.

Job Summary

Reporting to the Head of Data & Analytics the Lead Analytics Engineer is a senior individual contributor responsible for owning the analytics engineering layer withinGraphcoresdata platform. This role focuses on building and evolving curated data models trustedmetricsand well-documented semantic structures that enable reliable self-service analytics across the business. A key part of the role is partnering closely with stakeholders across business and technical functions to understand how teamsoperate build trusted relationships and translate real decision-making needs into clearusableand governed datasets that support reporting planning and operational insight.

The Team

The Data & Analytics team enables better decision-making acrossGraphcoreby building trusted data foundations scalableplatformsand high-quality data products. The team works across a broad range of business and technical domains partnering with colleagues throughout the company to improve access to reliable information strengthen operationalinsightand support efficient data-informed ways of working. Within this team the Lead Analytics Engineer owns a key part of the analytics workflow acting as a bridge between business stakeholders and data engineers to shape data models that reflect how the business works and can be adopted with confidence.

Responsibilities and Duties

• Own thedbttransformation layer buildingmaintainingand evolving data models that support reliable self-service analytics acrossGraphcore.

• Build strong working relationships with stakeholders across business and technical functions to understand priorities processesdefinitionsand decision-making needs.

• Work closely with stakeholders to discoverclarifyand challenge requirements turning ambiguous questions into well-structured analytical datasets and trusted metrics.

• Translate business processes and raw datasets into intuitiveflexibleand governed analytical models that support reportingplanningand operational decision-making.

• Design clear maintainable SQL models with a well-structured approach to naming layeringreuseand long-term sustainability.

• Partner with stakeholders to define document andmaintaintrusted metric and KPI logic ensuring consistency as requirements evolve.

• Implement robust testingvalidationand documentation practices indbtto improve data qualitytrustand discoverability.

• Work closely with Data Engineering toalign onsource data structures manage upstream schemachangesand support reliable downstream consumption.

• Establish andmaintainCI/CD practices for analytics engineering including automated checks reviewworkflowsand safe release processes.

• Optimisemodel performance and warehouse efficiency through pragmatic design choices including incremental approaches efficientjoinsand platform-aware tuning.

• Support self-service analytics by creating datasets that are easy to understand and consume with clear documentation and guidance for common use cases.

• Contribute to the effective use ofvisualisationand reporting tools by modelling data for dashboard performanceusabilityand consistency.

• Applyappropriate governanceand access control principles to analytical datasets working with colleagues to support secure andappropriate self-serviceaccess.

• Help shape analytics engineering standards and day-to-day practices within the wider Data & Analytics function through collaborationreviewand continuous improvement.

Candidate Profile

Essential

• Demonstrable experience building production-qualitydbtmodels that enable reliable self-service analytics.

• Strong SQL skills and experience designing maintainable transformation layers within a modern data platform.

• Proven ability to build strong relationships with stakeholders and work closely with business users to understand requirementsprocessesand data needs.

• Proven ability to translate business requirements and raw datasets into flexible intuitive data models that stakeholders can use confidently.

• Strong grasp of analytics engineering best practices including model layering documentationtestingand semantic consistency.

• Experience defining andmaintainingtrusted metrics KPIs and curated datasets for business use.

• Strong understanding of data quality changemanagementand the practices needed tomaintaintrust in analytical outputs.

• Experience applying CI/CD practices to analytics workflows including automated testing deploymentdisciplineand review processes.

• Experience working with relational databases and analytical warehouse technologies.

• Strong communicationskills including the ability to influence decisions challenge assumptionsconstructivelyand work effectively with both technical and non-technical stakeholders.

• A practical delivery-focused approach to problem solving.

Desirable

• Experience with data warehouse technologies such as RedshiftPostgreSQLorClickHouse.

• Experience supporting self-servicevisualisationand reporting tools such as SupersetMetabaseor similar platforms.

• Familiarity with semantic ormetrics-layertooling.

• Python experience including building lightweight data applications or utilities.

• Experience improving dataset discoverabilitydocumentationand adoption across anorganisation.

• Familiarity with data governance practices including access control and sensitive data handling.

• Experience working in a Git and pull-request based development workflow.

• Experience working in a fast-moving producttechnologyor engineering-led environment.