Engineering Manager, Data & AI Platform

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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

Position Overview

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 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.

Key Responsibilities

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 systemscovering 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 (KubernetesDocker).

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 you have a need that requires accommodation please contact us at. We will make every effort to respond to your request for accommodation as soon as possible. View our accessibility statement to learn more about eBays commitment to ensuring digital accessibility for people with disabilities.

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