Sr. Data Engineer - Depop

At eBay, we're more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts.

Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet.

Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all.

About the Team and Role

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 an experienced Senior Data Engineer to design and build scalable data systems that power critical experiences across eBay.

In this role, you will take technical ownership of complex, high-volume real-time and batch data pipelines, helping shape the architecture and engineering patterns behind our data platform. You will work with technologies including Kafka, Flink, Spark, Databricks, Airflow, dbt, and AWS to solve challenging distributed-data problems where scalability, performance, reliability, and accuracy are critical.

Beyond delivering individual projects, you will help drive technical decisions, identify architectural improvements, establish reusable engineering patterns, and mentor other engineers. You will partner closely with Data Science, Product, Analytics, and Engineering teams to translate complex requirements into scalable production systems.

What You Will Accomplish

• Design data systems at scale: Architect, build, and operate high-volume real-time and batch data pipelines using Kafka, Spark, and Flink.
• Drive streaming architecture: Design reliable, low-latency streaming solutions capable of processing large volumes of events while maintaining strong performance, fault tolerance, and data accuracy.
• Build reliable data workflows: Design and manage complex workflow orchestration using Airflow, improving reliability, observability, and operational efficiency.
• Strengthen data quality: Establish automated data validation, monitoring, and observability practices using technologies such as Monte Carlo, Great Expectations, or similar frameworks.
• Build cloud-native data platforms: Leverage Databricks and AWS to develop scalable data services and infrastructure that improve performance, reliability, developer productivity, and cost efficiency.
• Develop scalable transformations: Build maintainable transformation workflows using dbt and establish reusable patterns for producing trusted, high-quality datasets.
• Own complex initiatives end-to-end: Lead major data engineering projects from architecture and technical design through implementation, testing, deployment, monitoring, and production support.
• Solve complex distributed-systems problems: Diagnose performance bottlenecks, reliability issues, and scalability constraints across large-scale data systems and drive long-term architectural solutions.
• Influence technical direction: Contribute to architectural decisions, technical roadmaps, engineering standards, and technology choices across the broader data organization.
• Raise the engineering bar: Drive improvements in code quality, testing, observability, documentation, operational excellence, and engineering practices.
• Build reusable platforms: Develop common libraries, frameworks, and engineering patterns that can be leveraged across multiple teams and use cases.
• Mentor engineers: Provide technical guidance through design reviews, code reviews, troubleshooting, and mentorship while helping strengthen the overall engineering capabilities of the team.

What You Will Bring

• 8+ years of professional software engineering or data engineering experience, with significant experience designing, building, and operating large-scale production data pipelines, distributed systems, or backend data platforms.
• B.S. or M.S. in Computer Science or a related technical discipline, or equivalent practical experience.
• Strong programming skills in Python, Java, and/or Scala, with strong computer science and software engineering fundamentals.
• Deep understanding of data structures, algorithms, concurrency, multithreading, and distributed computing concepts.
• Strong hands-on experience designing and building distributed data processing systems using technologies such as Kafka, Spark, and Flink.
• Experience developing, deploying, optimizing, and supporting production data workloads using Databricks.
• Strong experience designing and operating cloud-native data solutions using AWS or a comparable cloud platform.
• Experience building and managing complex data workflows using Apache Airflow.
• Experience developing scalable data transformations using dbt or similar frameworks.
• Experience with data observability, quality, validation, and reliability technologies such as Monte Carlo, Great Expectations, or equivalent tools.
• Strong understanding of distributed systems architecture, relational databases, NoSQL technologies, data modeling, and large-scale data processing.
• Strong software engineering fundamentals, including object-oriented design, design patterns, automated testing, CI/CD, monitoring, and production support.
• Demonstrated experience troubleshooting complex performance, scalability, and reliability issues within production data systems.
• Experience making architectural decisions and driving technical projects involving multiple engineers or partner teams.
• Strong communication skills with the ability to influence technical decisions and collaborate effectively across Product, Data Science, Analytics, and Engineering organizations.

Ideally, You Will Also Have

• Experience architecting high-volume, low-latency streaming platforms using Kafka, Flink, Spark Structured Streaming, or similar technologies.
• Experience processing data at significant scale, including systems handling millions or billions of events or large-scale distributed datasets.
• A proven track record of building reusable libraries, frameworks, platforms, or common engineering patterns adopted by other engineers or teams.
• Experience optimizing distributed workloads for latency, throughput, scalability, reliability, and cost.
• Deep knowledge of Databricks and related technologies such as Delta Lake, Unity Catalog, Structured Streaming, or Lakehouse architectures.
• Experience working in ecommerce, marketplace, fintech, payments, or another high-scale consumer technology environment.
• Experience influencing architecture and engineering practices beyond your immediate projects.
• Experience mentoring engineers and providing technical leadership without requiring formal people-management responsibilities.
• A strong ownership mindset with a demonstrated ability to take ambiguous, technically complex initiatives from concept through successful production deployment.

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 talent@ebay.com. 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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