Graphcore
Lead Analytics Engineer
4d ago
EuropeLeaddata modelinganalyticsdata platform
Senior individual contributor responsible for analytics engineering within data platform, focusing on data models and self-service analytics.
About the company
- 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 .
Responsibilities
- 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.
Other
- 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 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.
- 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.
- 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.
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