The ideal candidate will be responsible for building, owning, and continuously improving the data foundations that support product analytics and decision-making. Beyond technical execution, this role requires strong autonomy, sound judgement, and the ability to understand and contextualise data within product and business objectives. We are seeking a Data Engineer who proactively identifies gaps, asks the right questions, and partners with stakeholders.

This role emphasises data reliability, event tracking, and scalable pipelines, with a strong focus on enabling analysts and product teams to derive trusted, decision-ready insights.

Detailed responsibilities and requirements are outlined in the attached document, including but not limited to:

• Building, owning, and evolving product data foundations to support analytics and experimentation.
• Designing and implementing event-level tracking across web and product surfaces, with a clear understanding of product intent and usage behaviour.
• Constructing and managing databases and associated data marts aligned to analytical and business needs.
• Developing and maintaining end-to-end data pipelines from source systems to the data warehouse and downstream dashboards.
• Ensuring data quality, reliability, and observability, including validation, monitoring, and proactive issue detection.
• Enabling scalable dashboards and self-serve analytics, with consideration for usability and interpretability.
• Collaborating closely with Product, Analytics, and Business teams to translate requirements into robust data solutions and to advise on best practices.

We are seeking candidates with:

• Demonstrated experience building data pipelines for analytics or product data.
• Strong proficiency in SQL and data modelling.
• Hands-on experience with event tracking, preferably with Google Analytics 4 and Big Query.
• Familiarity with AWS.
• A proven track record in ensuring data quality, reliability, and monitoring.
• The ability to work autonomously, understand product and business context, and make informed recommendations that improve data outcomes.