AI Analytics Engineer - Traffic

eBay•Austin, TX

About The Position

At eBay, we create trusted experiences that connect millions of buyers and sellers around the world. Behind those experiences is a platform that must be fast, reliable, and efficient at scale. The Traffic team plays a critical role in making that possible by transforming production telemetry into the insights and systems that help eBay’s platforms perform at their best. As an AI Analytics Engineer on the Traffic team, you will design and operate data products and analytical systems that explain, forecast, and optimize traffic behavior across eBay’s platforms. You will work across engineering, SRE, platform, application, and product teams to turn logs, metrics, traces, and traffic events into trusted datasets, scalable pipelines, and ML-ready features. This is an opportunity to work on high-impact challenges at the intersection of analytics, software engineering, and platform reliability while growing in a collaborative, modern data environment.

Requirements

Master’s degree in Computer Science, Engineering, Data Science, or a related field, or a Bachelor’s degree in a related field plus 2+ years of experience in analytics engineering, data engineering, ML data pipelines, or similar work.
Strong programming skills in Python and SQL, with experience in data modeling, debugging, and building reliable batch and/or streaming workflows.
Solid understanding of data warehouse or lakehouse concepts, distributed processing fundamentals, and Linux basics.
Familiarity with logs, metrics, and traces, and an understanding of how observability data can be used to improve platform performance and reliability.
Experience with technologies such as Spark, Databricks, Flink, Airflow, dbt, BigQuery, Snowflake, Kafka, or similar data and infrastructure tools.
A thoughtful, collaborative approach to problem-solving, with interest or experience in SLI/SLO reporting, incident analytics, performance attribution, or traffic-related infrastructure concepts.

Responsibilities

Turn large-scale telemetry and traffic signals into high-quality datasets and data products that improve visibility, trust, and decision-making across the organization.
Build scalable data and feature pipelines that power AI and forecasting use cases, including anomaly detection, incident prediction, capacity forecasting, and traffic attribution.
Deliver dashboards and analytical experiences that help teams understand latency, availability, error rates, saturation, regional behavior, and routing changes across the platform.
Use statistical and analytical techniques to identify meaningful patterns, quantify impact, and uncover the drivers of traffic and reliability outcomes.
Improve confidence in data by implementing quality checks, validation, freshness monitoring, lineage-minded practices, and alerting for critical analytics workflows.
Collaborate across Traffic Engineering, SRE, Platform, Application, and Product teams to connect production telemetry with customer experience, platform efficiency, and long-term scalability.