Staff Engineering Analytics Lead

We're looking for a Staff Engineering Analytics Lead to join our engineering team.

This is a unique opportunity for an experienced engineer who has shifted their focus to data and analytics. In this role, you will be crucial in defining, measuring, and improving engineering productivity across the organization, working with engineers on Site/App reliability, productivity and acting as key point of contact for collaboration between Data and Engineering.

You will be working alongside engineering colleagues in our journey of both Productivity Improvement and AI Adoption to better understand impact and correlations.

• Define and operationalise Site/App Reliability metrics. Work with Product/Eng/Data leaders to define key metrics for measuring site/app reliability and health. You will be responsible for agreeing on tools and embedding a deep understanding of these metrics throughout the organization.
• Define and operationalise productivity metrics. Work with engineering leaders to define key metrics for engineering productivity. You will be responsible for agreeing on tools and embedding a deep understanding of these metrics throughout the organization.
• Develop data integration pipelines. Create code that integrates our data warehouse with third-party tools, ensuring a seamless flow of information.
• Actively collaborate with engineering and business teams. Create a one-stop hub of metrics that a team can use to help understand the health and quality of its team.
• Enable data democratization. Make data accessible to everyone, regardless of their technical skills. Your work will empower teams across Product, Operations, and Leadership to confidently and independently explore data.

• An engineering background with a focus on data insights. You have a strong foundation in engineering principles and have developed a passion for leveraging data to solve problems.
• At least 5 years operating in the problem space of engineering operations, developer experience and software development lifecycle optimisations.
• Strong SQL and data modeling skills. You're proficient in dimensional modelling techniques (Kimball, Inmon, or similar) and can translate complex business needs into clean, maintainable data sets.
• Experience with analytical databases like Snowflake or BigQuery. You are comfortable investigating data warehouses and can perform simple coding tasks to extract and manipulate data.
• Proficiency with orchestration tools such as Airflow or Dagster.
• Familiarity with modern ELT tools, version control, and CI/CD best practices in a data context.
• Exceptional communication skills. You can effectively explain data insights to non-technical stakeholders (e.g., Product Managers, business experts) and communicate technical details to fellow engineers.
• Self-directed problem solver. You can navigate through operational and organisational ambiguity, bringing clarity while identifying opportunities and prioritising tasks for maximum impact.
• Global Impact- Collaborate with engineering core leadership teams on critical projects and defining key success metrics to drive alignment and impact across the global engineering teams.