Our mission is to make fashion circular and to create an inclusive environment where everyone is welcome, no matter who they are or where they’re from. Just as our platform connects people globally, we believe our workplace should reflect the diversity of the communities we serve. We thrive on the power of different perspectives and experiences, knowing they drive innovation and bring us closer to our users. We’re proud to be an equal opportunity employer, providing employment opportunities without regard to age, ethnicity, religion or belief, gender identity, sex, sexual orientation, disability, pregnancy or maternity, marriage and civil partnership, or any other protected status. We’re continuously evolving our recruitment processes to ensure fairness and are open to accommodating any needs you might have.
If, due to a disability, you need adjustments to complete the application, please let us know by sending an email with your name, the role to which you would like to apply, and the type of support you need to complete the application to [email protected]. For any other non-disability related questions, please reach out to our Talent Partners.
Job Description
Depop is looking for a Senior Data Platform Engineer to help architect, build, and scale our data platform, empowering teams with self-serve capabilities. You’ll combine hands-on technical expertise with strategic thinking to shape the foundation that supports our products and customers, enabling teams to own, process, query, discover and manage their data with efficiency and ease.
You will work closely with people from a wide variety of domains, as well as our ML, MLOps, Analytics Engineering, Data Science, Insights, MarTech, Platform and other engineering teams. You will help manage our growing data needs and support increasingly complex business problems by building and promoting self-service tools and data best practices that will be used across the organisation, including taking ownership of our data transformation and orchestration tooling; batch and streaming infrastructure and exploration tools (Databricks, Airflow, dbt) and look after our Datalake (ingestion, storage, governance, privacy).
You’ll work with a modern, cutting-edge data stack and play a key role in shaping data-as-a-product practices. As we scale to meet Depop’s bold growth ambitions, you’ll help introduce the next generation of data capabilities.
Want to find out more about Depop & our engineering team? We write about technology, people and smart engineering right here: https://engineering.depop.com/
What You’ll Do
- Pave a path for data as a product: Champion data as a first-class citizen by introducing robust data capabilities, observability and governance into the platform to enable decision making and ultimately better experience for Depop’s end users.
- Software Engineering:Develop microservices, libraries, platforms and tools
- Technical Design:Architect and evolve platform services that enable teams to work with data efficiently and securely at scale.
- Lead Technological Initiatives:You will lead large, complex initiatives that will further enable our data users by setting the vision and roadmap, gathering requirements and defining the scope and success metrics for your initiative all while embracing a platform as a product approach.
- Uphold Operational Excellence:Automating infrastructure and monitoring, leading incident response and root cause analysis, and continuously improving the health and performance of our data platform. Champion scalable standard processes through automation, clear documentation, and knowledge sharing via tutorials and training sessions.
- Stakeholder Collaboration:Work closely to establish trusted relationships with both technical and non-technical stakeholders to define, design, and implement solutions within our data platform. You will also contribute to group and peer-level initiatives to improve our data platform as a whole.
- Invest in others’ growth:Mentoring and sharing knowledge, helping elevate technical standards and fostering a culture of continuous improvement.
- Build on our Engineering Culture:Take an active role in improving the engineering culture at Depop and encourage others around you to follow these values.
What You’ll Bring
- Advanced knowledge in a high-level programming language (e.g. Python, Scala) with proven foundation in software engineering best practices – testing, clean coding standards, code reviews, pair programming, automation-first mindset
- Proven hands-on experience designing, building, and scaling complex data systems and backend infrastructure in production environments within a modern cloud-based environment (AWS, GCP, Azure)
- Ability to think strategically and pragmatically: you can define long-term technical strategies and execute rapidly.
- Excellent communication and collaboration skills; you’re comfortable influencing technical direction and aligning diverse stakeholders across the organisation – ML engineers, analysts, analytics engineers, Product, Marketing and leadership and have a strong grasp of their needs and how they operate
- Expertise in managing and integrating data orchestration and processing tools & platforms such as Databricks, Airflow, dbt, Kafka or similar
- Experience with agentic coding tools (Claude Code, Cursor etc)
- A passion for learning new things and keeping on top of the latest developments and technologies in our field. We take pride in our learning and make sure to have dedicated time set aside for our growth and development
Nice To Have
- DevOps experience building CI/CD pipelines (Jenkins, GitHub Actions), IaC (Terraform)
- Experience working with containerisation technologies – Docker, Kubernetes
- Previous hands-on expertise with Spark
- Advanced experience working and understanding the tradeoffs of at least one of the following Data Lake table/file formats: Delta Lake, Parquet, Iceberg, Hudi
- Experience working with lakehouse / medallion architectures in Databricks
- Streaming Knowledge: Experience with Kafka/Flink/Confluent or other streaming ecosystems, with a solid understanding of their components
- Experience working with data governance, observability and catalogue solutions (e.g. Monte Carlo, Atlan, Datahub) enabling rich data lineage, data contracts, SLA/SLO, tagging and data quality monitoring capabilities