Data Engineer Manager - Depop at Ebay in Toronto

Job description

• Depop is a peer-to-peer circular fashion marketplace where anyone can buy, sell and discover secondhand fashion. Depop's mission is simple: to make fashion circular by making secondhand as exciting and rewarding as buying new.
• Founded in 2011, Depop’s diverse community has helped move resale into the mainstream, where buying secondhand is no longer an alternative, but how people of different ages now engage with fashion. Today, more than 56 million registered users come to Depop to find great value, express their own personal style and give clothes a longer life. We believe that everything you want already exists, and our role is to help people discover it. From everyday essentials to vintage and designer finds, Depop brings together a wide range of affordable styles in one place. It’s a marketplace where anyone can clear out their wardrobe or build a business, explore their style, and take part in a more circular way to shop.
• Powered by a team of over 500 people, our company is headquartered in London, with offices in New York. For more information, visit our News Room here.
• 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.

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.

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

Skills

Required

• Leadership and team development
• Data engineering or software engineering
• Distributed systems
• Cloud-based data platforms
• Kafka
• Spark
• Flink
• Databricks
• Airflow
• dbt
• AWS
• Cross-functional collaboration