Sr. Data Engineer Manager

eBay•Toronto, ON

22h

About The Position

We are looking for a passionate and experienced Data Engineering Manager to lead a team building scalable, reliable data solutions that power critical experiences across eBay. This team plays a central role in eBay’s data ecosystem, enabling trusted real-time insights, analytics, experimentation, and personalized experiences for millions of customers across the marketplace. As part of this focused C2C strategy, eBay continues to invest in the future of circular commerce and the next generation of conscious consumers. With the recent acquisition of Depop, a leading consumer-to-consumer fashion marketplace with a highly engaged Gen Z and Millennial customer base, eBay is expanding its portfolio of complementary C2C businesses while preserving the distinct brand, community, and product experience that make Depop unique. In this role, you will lead and grow a team of data engineers responsible for architecting, building, and operating high-performance real-time and batch data pipelines and platforms. You will set technical direction, drive execution across complex initiatives, and partner closely with Product, Data Science, Analytics, and Engineering leaders to translate business priorities into durable, high-quality data solutions. This is an opportunity to combine people leadership, technical depth, and organizational influence in a highly scaled environment. You will help shape modern data engineering practices across technologies such as Kafka, Flink, Spark, Databricks, Airflow, dbt, and AWS, while fostering an inclusive, high-performing team culture focused on innovation, operational excellence, and continuous learning.

Requirements

Experience leading and developing engineering teams, with a track record of delivering complex data, platform, or backend initiatives in fast-paced and highly collaborative environments.
Strong technical foundation in data engineering or software engineering, including experience with distributed systems, large-scale batch and streaming pipelines, and cloud-based data platforms.
Hands-on knowledge of technologies such as Kafka, Spark, Flink, Databricks, Airflow, dbt, AWS, or comparable modern data engineering tools, with the ability to guide architecture and support strong technical execution.
Experience establishing engineering best practices across system design, data quality, observability, testing, production support, and operational excellence for large-scale data platforms.
Strong cross-functional collaboration, communication, and prioritization skills, with the ability to align engineering investments to product and business goals and influence stakeholders across multiple disciplines.
Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.

Nice To Haves

Experience in ecommerce, marketplace, fintech, payments, or other high-scale environments is a plus.

Responsibilities

Lead, coach, and develop a team of data engineers, creating an inclusive and high-performing environment where team members grow their technical depth, expand ownership, and deliver meaningful business impact.
Drive the design and delivery of scalable real-time and batch data pipelines and platforms that enable trusted, timely, and high-volume data consumption across product, analytics, and machine learning use cases.
Define and execute the roadmap for modern data engineering capabilities, including streaming, orchestration, transformation, observability, and cloud-native platform development using technologies such as Kafka, Spark, Flink, Airflow, dbt, Databricks, and AWS.
Partner cross-functionally with Product, Data Science, Analytics, and Engineering teams to prioritize investments, translate evolving requirements into robust technical solutions, and deliver data products that improve customer and business outcomes.
Raise the engineering bar by establishing strong practices for architecture, code quality, testing, documentation, operational readiness, and data reliability, while proactively improving scalability, performance, and cost efficiency.
Own execution across major initiatives from planning through production operations, balancing near-term delivery with long-term platform health and influencing technical decisions that improve reuse, maintainability, and speed across teams.