Engineering Manager – Data & Analytics Engineering

Company Description

Depop is a peer-to-peer circular fashion marketplace where anyone can buy, sell and discover secondhand fashion. Our 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.

Powered by a team of over 500 people, our company is headquartered in London, with offices in New York. In 2021, Depop became a wholly-owned subsidiary of Etsy - the global marketplace for unique and creative goods - and continues to operate as a standalone company. For more information, visit www.depop.com

We aim 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.

AI Disclosure: We use AI tools (Google Gemini) to help our team source and review applications for roles with a high volume of applications. These tools assist our recruiters in identifying great talent but do not replace human decision-making. At Depop, every hiring decision is made by a human.

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 adjustments@depop.com .

We’re looking for a customer-focused, AI-forward Engineering Manager – Data & Analytics Engineering to lead and evolve the team responsible for building Depop’s trusted analytical data foundations.

In this role, you’ll help shape the future of data and analytics engineering at Depop, building high-quality, reusable data assets and scalable data pipelines that power analytics, machine learning, and AI-driven decision-making across the company.

You’ll lead a multidisciplinary team of Data Engineers and Analytics Engineers responsible for developing and maintaining core data models, transformation pipelines, semantic layers, and the foundations that enable trustworthy self-service and agentic analytics.

You'll be an experienced engineering leader who is passionate about developing people, building effective teams, and creating an environment where engineers can do their best work. You’ll bring strong experience in coaching, developing engineers, and evolving engineering practices to help teams grow, collaborate, and deliver consistently.

You’ll also bring an AI-first mindset, actively exploring how AI can transform engineering workflows, improve productivity, and create new opportunities for engineers to innovate and develop their skills.

Working closely with Data Science, Data Platform, Data Governance, and Product leadership, you’ll help establish clear technical direction, strengthen collaboration, and ensure our data and analytics engineering capabilities continue to evolve as Depop scales.

Responsibilities

• •Lead, mentor, and develop a team of Data Engineers and Analytics Engineers, fostering a culture of ownership, accountability, collaboration, and continuous improvement.
• •Build a high-performing team through effective coaching, constructive feedback, clear expectations, and meaningful career development.
• •Drive strong engineering execution through clear prioritisation, effective planning, and predictable, sustainable delivery.
• •Champion an AI-first engineering culture, embedding AI-assisted development, automation, and agentic workflows to improve productivity, quality, and engineering effectiveness.
• •Define and drive the technical roadmap for data and analytics engineering, balancing business priorities with long-term scalability, maintainability, and technical excellence.
• •Lead the development of trusted, reusable analytical data models, scalable data pipelines, and semantic layers to power analytics, machine learning, and AI-driven decision-making.
• •Establish strong engineering practices across data quality, automated testing, CI/CD, documentation, observability, and governance.
• •Partner with stakeholders across Data Science, Product, Finance, and Engineering to align priorities, establish ownership, and deliver impactful data solutions.
• •Empower engineers to take initiative, drive technical improvements, and continuously evolve how the team works.

Qualifications

• •Proven experience managing data engineering, analytics engineering, or multidisciplinary data teams, ideally in a consumer technology or marketplace environment.
• •Strong track record of developing high-performing engineering teams through effective coaching, constructive feedback, clear expectations, and continuous improvement
• •An AI-forward approach, with practical experience adopting AI-assisted development, automation, or agentic workflows to improve engineering productivity and quality.
• •Strong experience driving engineering execution, translating technical strategy into clear priorities, realistic plans, and measurable outcomes.
• •Deep understanding of modern data architectures, including lakehouses, data warehousing, ETL/ELT pipelines, orchestration, and dimensional data modelling.
• •Hands-on experience with modern data technologies such as dbt, Databricks, Spark, SQL, and Python.
• •Understanding of semantic layers, data governance, and the foundations required for trustworthy AI-powered analytics.
• •Strong technical foundation, with experience designing or building data systems and championing engineering best practices.
• •Excellent communication, stakeholder management, and interpersonal skills, with the ability to navigate challenging conversations and build trusted relationships.

Bonus Points

• •Experience building or enabling AI-powered analytics, conversational BI, or agentic data workflows.
• •Experience implementing semantic layers, metrics frameworks, or self-service analytics capabilities.
• •Familiarity with AI coding assistants, agent-based development workflows, and evaluation frameworks for AI-generated outputs.
• •Experience with large-scale data modelling in Databricks and dbt.
• •Experience with data contracts, lineage, and modern data governance practices.
• •Experience supporting machine learning or experimentation through trusted analytical data products.
• •Experience in marketplaces, fashion, or sustainability-driven technology.

Additional Information

Health + Mental Wellbeing

• •PMI and cash plan healthcare access with Bupa
• •Subsidised counselling and coaching with Self Space
• •Cycle to Work scheme with options from Evans or the Green Commute Initiative
• •Employee Assistance Programme (EAP) for 24/7 confidential support
• •Mental Health First Aiders across the business for support and signposting

Work/Life Balance:

• •25 days of annual leave with the option to carry over up to 5 days
• •Impact hours: Up to 2 days of additional paid leave per year for volunteering
• •Fully paid 4-week sabbatical after completion of 5 years of consecutive service with Depop, to give you a chance to recharge or do something you love.
• •Flexible Working: