Lead Analytics Engineer

About us  

Graphcore is one of the world’s leading innovators in Artificial Intelligence compute . 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 Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone.

Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore brings together deep expertise to solve complex problems and deliver meaningful progress in AI compute .

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 within Graphcore’s data platform. This role focuses on building and evolving curated data models, trusted metrics and 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 teams operate , build trusted relationships, and translate real decision-making needs into clear, usable and governed datasets that support reporting, planning and operational insight.

The Team  

The Data & Analytics team enables better decision-making across Graphcore by building trusted data foundations, scalable platforms and 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 operational insight and 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 the dbt transformation layer, building, maintaining and evolving data models that support reliable self-service analytics across Graphcore .

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

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

• Translate business processes and raw datasets into intuitive, flexible and governed analytical models that support reporting, planning and operational decision-making.

• Design clear, maintainable SQL models with a well-structured approach to naming, layering, reuse and long-term sustainability.

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

• Implement robust testing, validation and documentation practices in dbt to improve data quality, trust and discoverability.

• Work closely with Data Engineering to align on source data structures, manage upstream schema changes and support reliable downstream consumption.

• Establish and maintain CI/CD practices for analytics engineering, including automated checks, review workflows and safe release processes.

• Optimise model performance and warehouse efficiency through pragmatic design choices, including incremental approaches, efficient joins and 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 of visualisation and reporting tools by modelling data for dashboard performance, usability and consistency.

• Apply appropriate governance and access control principles to analytical datasets, working with colleagues to support secure and appropriate self-service access.

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

Candidate Profile  

Essential  

• Demonstrable experience building production-quality dbt models 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 requirements, processes and 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, documentation, testing and semantic consistency.

• Experience defining and maintaining trusted metrics, KPIs and curated datasets for business use.

• Strong understanding of data quality, change management and the practices needed to maintain trust in analytical outputs.

• Experience applying CI/CD practices to analytics workflows, including automated testing, deployment discipline and review processes.

• Experience working with relational databases and analytical warehouse technologies.

• Strong communication skills, including the ability to influence decisions, challenge assumptions constructively and 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 Redshift, PostgreSQL or ClickHouse .

• Experience supporting self-service visualisation and reporting tools such as Superset, Metabase or similar platforms.

• Familiarity with semantic or metrics-layer tooling.

• Python experience, including building lightweight data applications or utilities.

• Experience improving dataset discoverability, documentation and adoption across an organisation .

• 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 product, technology or engineering-led environment.