Data Engineer Manager - Depop

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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.

About the team and the role:

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.

A key part of the role will be ensuring that data is treated as a trusted product. You will help establish strong practices around data quality, observability, ownership, discoverability, and governance, enabling teams to understand and rely on the data they produce and consume. You will drive improvements in how data issues are detected, diagnosed, and resolved, while promoting clear accountability across the data lifecycle.

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.

What you will accomplish:

• 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.
• Define and drive the long-term technical vision and roadmap for the team, translating business and product priorities into investments across data platforms, streaming, orchestration, transformation, quality, and observability.
• 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.
• Establish strong data reliability practices, including monitoring, validation, alerting, and incident management, improving the ability to proactively detect, diagnose, and resolve data issues.
• Champion data-as-a-product principles by promoting clear ownership, quality expectations, discoverability, and accountability across data producers and consumers.
• Drive scalable approaches to data quality, observability, ownership, and governance that improve trust and accountability across the data lifecycle.
• 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.

What you will bring:

• 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.
• Demonstrated ability to define technical vision and strategy, build alignment around a roadmap, and drive execution across multiple teams and stakeholders.
• 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.
• Experience with data reliability concepts such as monitoring, quality validation, incident management, service-level expectations, and reducing time to detect and resolve data issues.
• Familiarity with modern approaches to data contracts, metadata, lineage, discoverability, and governance, and an understanding of how these capabilities improve trust and accountability across the data lifecycle.
• Understanding of data security, privacy, and governance principles and how they should be incorporated into modern 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.

Additional Details

This job posting relates to an existing vacancy within eBay.

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, 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.

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