At a Glance
- Tasks: Lead the analytics engineering layer, building data models for reliable self-service analytics.
- Company: Join Graphcore, a leader in AI innovation and part of the SoftBank Group.
- Benefits: Competitive salary, dynamic work environment, and opportunities for professional growth.
- Why this job: Shape the future of AI by enabling data-driven decision-making across industries.
- Qualifications: Experience with dbt models, strong SQL skills, and ability to collaborate with stakeholders.
The predicted salary is between 70000 - 90000 £ per year.
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.
Lead Analytics Engineer in London employer: graphcore
Graphcore is an exceptional employer, offering a dynamic work environment that fosters innovation and collaboration among diverse teams. Employees benefit from a culture that prioritises professional growth, with opportunities to engage in cutting-edge AI projects while being part of the prestigious SoftBank Group. Located in a vibrant tech hub, Graphcore provides a unique chance to contribute to transformative technologies that shape the future of AI, all while enjoying a supportive atmosphere that values creativity and teamwork.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Analytics Engineer in London
✨Tip Number 1
Network like a pro! Reach out to people in your industry on LinkedIn or at events. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects and achievements. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios related to analytics engineering. The more you rehearse, the more confident you'll feel when it’s showtime!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive!
We think you need these skills to ace Lead Analytics Engineer in London
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your experience with dbt models and SQL in your application. We want to see how you've built reliable self-service analytics before, so don’t hold back on the details!
Connect the Dots:When you describe your past roles, focus on how you’ve built relationships with stakeholders. We love seeing examples of how you’ve translated business needs into actionable data models.
Keep It Clear and Concise:Your application should be easy to read and understand. Use clear language and structure your thoughts well. Remember, we’re looking for someone who can create intuitive datasets, so show us you can do that right from the start!
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves.
How to prepare for a job interview at graphcore
✨Know Your dbt Inside Out
Make sure you’re well-versed in building production-quality dbt models. Be ready to discuss your experience with dbt transformations and how they enable reliable self-service analytics. Prepare examples of how you've tackled challenges in this area.
✨Build Relationships Before the Interview
Graphcore values strong relationships across teams, so think about how you can demonstrate your ability to connect with stakeholders. Share past experiences where you’ve successfully collaborated with both technical and non-technical teams to understand their data needs.
✨Showcase Your SQL Skills
Brush up on your SQL skills and be prepared to discuss how you design maintainable transformation layers. You might even want to practice writing some SQL queries or models that could be relevant to the role, as this will show your hands-on expertise.
✨Emphasise Data Quality and Governance
Be ready to talk about your understanding of data quality and governance practices. Discuss how you’ve implemented testing, validation, and documentation in your previous roles to maintain trust in analytical outputs. This will highlight your commitment to high standards in analytics engineering.
