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This role is responsible for maintaining, hardening, and extending our event processing infrastructure, focusing on adding targeted real-time processing capabilities to support analytics and informed decision-making, our decisions are deeply data-driven. This critical role is centered on maintaining, hardening, and extending our event processing infrastructure, with a primary focus on adding targeted real-time processing capabilities. Your work will directly support our analytics and enable informed, data-driven business decision-making based on production data. Specifically, you will be responsible for the core data component of capturing the clickstream events from the website, which is the foundational functionality for all our analytics.

Looking forward, this role will help lead the team into its next phase: improving data quality and developer experience, investing in modern table formats (Iceberg), empowering analysts and data scientists, and continuously reducing cost and operational friction.

This is a player-coach role, combining hands-on technical leadership with ownership, prioritization, and mentorship.

What You'll Do
Own the end-to-end real-time and event data domain, including ingestion, processing, and downstream consumption.

Ensure stability, correctness, and observability of existing streaming and near-real-time pipelines.
Lead the design and implementation of select real-time processing components to support analytics and decision-making use cases.
Define clear ownership, SLAs, and best practices for event data usage across the company.
Lead the teams investment in modern data infrastructure
Drive architectural decisions with a long-term view on maintainability, cost, and scalability.
Team Leadership & Execution

Lead, mentor, and support a team of data engineers; set technical standards and review designs and code.
Act as a hands-on contributor in critical areas of the system.
Own planning and prioritization, balancing new investments with platform stability and technical debt.
Promote a culture of quality, documentation, and operational excellence.
Partner with Data Analytics and Data Science teams to improve data accessibility, trust, and self-service capabilities.
Improve observability across the data platform (data quality, freshness, lineage, failures).
Explore and adopt AI-assisted development tools to improve engineering velocity and code quality.
Stay current with emerging technologies in data engineering and analytics, and evaluate their relevance to the platform.