Data Platform Engineering Manager

As the Engineering Manager for Realtime Data Platform, a team within the Knowledge Platform, you will oversee large-scale, cross-functional initiatives that impact the entire data ecosystem. You will be responsible for defining the multi-year vision and technical roadmap for all real-time data analysis and serving needs, ensuring they are aligned with broader business objectives and product goals. A key aspect of your role will also include scaling the engineering teams, defining new roles, and establishing best practices for communication and collaboration across multiple sub-teams.

Responsibilities

Provide visionary technical leadership and define a clear 1-3 year strategic roadmap for the Realtime Data Platform.; Lead the multi-year effort to modernize our core data platform, introducing real-time analytical processing at petabyte scale.; Partner with product teams to enable new data-driven features, such as AI-powered applications or real-time dashboards, by ensuring the underlying platform capabilities are in place.; Successfully scale and structure the engineering teams, including hiring new talent and mentoring managers and senior individual contributors.; Cultivate and maintain strong relationships with product and other engineering teams, serving as a trusted technical advisor on all things Data and AI.

Requirements

A minimum of 8 years of experience in data or software engineering, with at least 3 years in a leadership or management role.; Proven experience successfully managing and scaling an engineering team of 10+ people, including managers and senior individual contributors.; Demonstrated ability to define and execute a technical strategy that led to a significant business outcome or operational improvement.; Strong command of the data and software engineering domains, with a focus on architecture and strategy.; Deep understanding of distributed data processing technologies (e.g., Apache Spark, Databricks) and event streaming (e.g., Kafka).; Solid knowledge of modern data storage and serving technologies, such as CubeJS, ElasticSearch, and Clickhouse, and more.; Familiarity with observability tooling such as Splunk, Grafana, and Prometheus.

Nice to have

Experience within the financial domain.; Hands-on design experience in crafting data processing patterns for a modern Lakehouse architecture.; Excellent written and verbal communication skills tailored for diverse audiences (leadership, users, company-wide).; Ability to rapidly evaluate various technologies and conduct proof of concepts to drive architecture design.; Experience thriving in a complex environment.