Data Engineering Manager

A market-leading global technology company delivering high-scale digital communications and messaging ecosystems worldwide. Employing hundreds of professionals globally, the company’s platforms serve hundreds of millions of active users, processing tens of billions of daily events across mission-critical, low-latency distributed networks. The organization pairs robust enterprise stability with massive engineering scale, offering high-impact technical challenges across Big Data infrastructure, real-time analytics, and applied Machine Learning. Located in the Central District adjacent to the light rail, operating on a hybrid model with two days working from home. Role Description- Serving as Data & ML Engineering Group Lead / Team Lead, reporting directly to the VP of Engineering, leading a dedicated high-caliber team of 10–11 Data and Machine Learning Engineers. Focusing primarily on people leadership, technical mentorship, execution strategy, and organizational growth across the data and ML functions. Taking full ownership of an enterprise-grade Self-Service Data Platform operating at immense scale, processing tens of billions of daily events. Guiding the architecture, scalability, and resilience of high-throughput Batch & Streaming data pipelines (Spark, Flink, Storm) and cloud infrastructure (AWS, Kubernetes). Leading production-grade Machine Learning systems supporting Recommendation, Ranking, and Predictive modeling (utilizing PyTorch, TensorFlow, and AWS SageMaker). Ensuring system reliability, performance benchmarking, high availability, and comprehensive observability across distributed Data and ML environments. Requirements- 3 years of proven leadership experience managing large engineering teams (10+ engineers) or serving as an Engineering Group / Domain Lead – Mandatory 6 years of hands-on professional experience as a Data Engineer or Machine Learning Engineer – Mandatory 7 years of deep, practical software engineering proficiency in BOTH Python AND Java – Mandatory Proven track record leading high-scale Big Data pipelines (Batch & Real-time streaming) using technologies such as Spark, Flink, or Storm – Mandatory Hands-on familiarity with deploying and maintaining Machine Learning systems in production (Recommendation, Ranking, Prediction) – Mandatory Solid background in cloud-native distributed environments (AWS, Kubernetes, containerized workflows) – Mandatory Exceptional leadership, cross-functional orchestration, and fluent technical communication skills in English – Mandatory Show more Show less