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We are seeking an experienced Engineering Manager for our Data Platform team to lead the architecture, scaling, and operation of our core data in
We are seeking an experienced Engineering Manager for our Data & AI Platform team to lead the architecture, scaling, and operation of our core data/AI infrastructure. In this role, you will manage a team of platform engineers responsible for several distinct, mission-critical pillars: our high-throughput batch eco-system, our real-time, low-latency streaming platform, our model training / finetuning system and our online/offline model inference system.
You will bridge the gap between deep technical infrastructure investments and strategic people leadership, ensuring our data lake / AI ecosystem is reliable, cost-efficient, and secure.
Technical Vision & Infrastructure Ownership
Batch Processing Ecosystem: Direct the scaling, optimization, and upgrades of distributed batch computing infrastructure (e.g., Apache Spark ecosystem, Hadoop/HDFS).
Streaming Infrastructure: Own the reliability, partitioning strategies, and architecture of our event-driven streaming backbone (e.g., Apache Kafka, Flink) to power real-time analytics and microservices.
Storage & Lakehouse Evolution: Oversee the data storage layer, optimizing open table formats (e.g., Iceberg) for both fast stream writes and heavy batch analytical queries.
Machine Learning Infrastructure: Lead the design, scaling, and reliability of end-to-end ML and LLM platforms covering training and fine-tuning pipelines, model registry, batch and real-time inference, and online/offline serving. Optimize distributed training and LLM inference performance for low latency, high throughput, scalable model serving, and cost-efficient GPU utilization.
Security & Governance: Drive enterprise-grade data security initiatives, including automated compliance, data cataloging, and scalable column/row-level encryption framework deployment across both streaming and batch domains.
Operational Excellence & Financial Engineering
Infrastructure Efficiency: Lead cost-optimization and capacity-planning initiatives to manage massive cloud compute and storage footprints effectively.
SLA/SLO Accountability: Define and enforce strict availability, data freshness, and processing latency SLAs for downstream data consumers.
Team Leadership & Delivery
People Management: Mentor, coach, and grow senior and principal engineers specializing in distributed systems and data infrastructure.
Cross-Functional Alignment: Collaborate closely with Data Engineering, Data Science, Core Platform, and Product teams to treat the data & AI platform as an internal product, simplifying how other teams ingest, store, and consume data; speed up the new AI technology adoption in business use cases.
Qualifications & Skills
Management & Leadership Experience
People Leadership: 3+ years of experience managing high-performing engineering teams in the data & AI platform, infrastructure, or distributed systems space.
Proven Track Record: Experience managing large-scale infrastructure migrations (e.g., Spark major version upgrades, cloud migrations, or moving from batch to real-time event streaming architectures).
Technical Domain Expertise
Batch Technologies: Deep technical depth in Apache Spark, Hadoop, and optimization of large-scale distributed jobs.
Streaming & Messaging: Expert-level understanding of Apache Kafka, Flink, or similar event-streaming and pub/sub patterns (including consumer group management, schema registries, and stream-processing windowing techniques).
Storage & Query Engines: Hands-on experience with modern lakehouse formats (Iceberg, Delta) and distributed SQL query engines (Trino/Presto).
Model Training Platforms & Pipelines: Expertise in building scalable, reproducible training/finetuning systems—covering data/feature ingestion, distributed training orchestration, experiment tracking, model registry, and CI/CD for ML (e.g., PyTorch, Ray, Kubernetes, MLflow).
Inference & Serving Infrastructure: Hands-on experience in deploying and operating low-latency, high-throughput model and LLM serving stacks, including AI control plane capabilities for model deployment, routing, policy enforcement, observability, traffic management, and cost-efficient inference operations using platforms such as Triton, vLLM, and Kubernetes.
Languages & Infrastructure: Strong foundations in Java, Scala, Python, or Go, along with infrastructure-as-code tooling (Kubernetes,Docker).
Additional Details
eBay is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, gender identity, veteran status, and disability, or other legally protected status. If you have a need that requires accommodation, please contact us at [email protected]. We will make every effort to respond to your request for accommodation as soon as possible. View our accessibility statement to learn more about eBay's commitment to ensuring digital accessibility for people with disabilities.
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