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 insightand 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. 

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 Data Engineer is a senior individual contributor responsible for leading a key area of Graphcore’s data platform and engineering practices. This role combines hands-on technical delivery with technical leadership across data pipelines, platform capabilities and data products that support analytics, reportingand operational decision-making. Working closely with stakeholders across technical and business functions, the Lead Data Engineer helps shape the direction of the data platform, drives improvements to reliability, scalability and governance, and enables teams across Graphcore to make better use of trusted data. 

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 insightand support efficient, data-informed ways of working. Within this team, the Lead Data Engineer plays a key role in evolving the platform, setting engineering standards and delivering robust solutions that scale with business needs. 

Responsibilities and Duties 

• Lead the design, build and evolution of robust data pipelines and platform services that support analytics, reporting and operational use cases across Graphcore. 

• Own the data engineering stack, planning and delivering improvements to reliability, scalability, maintainability, performance and security. 

• Build and operate Python-based batch and streaming workflows, with clear approaches to orchestration, testing, deployment, monitoring and incident resolution. 

• Design and implement data solutions on AWS using services such as S3, Lambda, Aurora PostgreSQL, Athena, Glue and Redshift, ensuring they are secure, resilient and cost-conscious. 

• Define and apply engineering standards for data quality, observability, documentation, release processes and operational support. 

• Partner with analysts, engineers and business stakeholders to translate requirements into trusted datasets, well-structured data models and reusable data products. 

• Drive improvements to platform resilience through approaches such as idempotent processing, retry and recovery mechanisms, buffering strategies and backfill or replay capabilities. 

• Lead technical decision-making in your area by reviewing designs and code, sharing expertise and helping to raise the quality bar for data engineering across the team. 

• Build and maintain CI/CD workflows and development practices that enable safe, repeatable and efficient delivery of data infrastructure and workflows. 

• Ensure appropriate data protection and access controls are in place, including least-privilege access, secure secrets handling and suitable database permissions. 

• Contribute to the development of internal tools and lightweight applications that improve access to data and support self-serve workflows. 

• Work across teams to identify opportunities for platform and process improvements, helping shape the direction of data engineering within the wider Data & Analytics function. 

Candidate Profile 

Essential 

• Strong experience designing, building and operating production-grade data pipelines and data platforms in Python. 

• Strong hands-on experience with modern data orchestration, testing, deployment and monitoring practices in a production environment. 

• Experience building solutions on AWS data services, including storage, processing and query technologies. 

• Strong understanding of data modelling, data quality, schema design and performance optimisation across relational and analytical systems. 

• Experience designing reliable data systems that recover gracefully from failure and operate effectively in real-world production conditions. 

• Experience working with batch and streaming data pipelines, including operational support, troubleshooting and continuous improvement. 

• Strong knowledge of security and access control principles for data platforms, including IAM, database permissions and secure handling of credentials and secrets. 

• Experience providing technical leadership as a senior individual contributor through design reviews, code reviews, standards-setting and mentoring of others. 

• Ability to work effectively with both technical and non-technical stakeholders, turning business needs into practical, scalable data solutions. 

• Strong communication skills, with the ability to explain technical decisions clearly and influence outcomes across teams. 

Desirable 

• Experience with Prefect or a similar workflow orchestration platform. 

• Experience with streaming or data collection technologies. 

• Experience with PostgreSQL, Redshift, ClickHouse or similar database and warehouse technologies. 

• Experience with CI/CD tooling and Infrastructure as Code approaches. 

• Experience building lightweight internal tools or data applications using Python frameworks such as Streamlit or Flask. 

• Familiarity with dbt and working models that combine data engineering and analytics engineering. 

• Understanding of operational best practices for cloud-based data platforms, including cost optimisation and observability. 

• Experience working in a fast-moving product, technology or engineering-led environment.