Engineering Manager – Data & Analytics Engineering

DEPOP; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Benefits offered by this job

Health + Mental Wellbeing

Sabbatical program

Hybrid/Remote options

Learning budget

Life Insurance

Job summary

Depop is seeking a customer-focused, AI-forward Engineering Manager - Data & Analytics Engineering to lead the team building trusted analytical data foundations. You will shape data and analytics engineering at Depop, creating reusable data assets and scalable pipelines powering analytics, ML, and AI-driven decisions.

You will mentor Data and Analytics Engineers, establish strong engineering practices, and champion AI-first workflows that improve productivity.

Qualifications

• Proven experience leading data/analytics engineering teams.
• Strong coaching and feedback skills with focus on growth.
• AI-forward approach to development, automation, and workflows.
• Experience turning technical strategy into actionable plans.
• Expertise in modern data architectures (lakehouses, data warehousing).
• Hands-on with data tools like dbt, Databricks, Spark, SQL, Python.

Responsibilities

• Lead and develop a team of Data and Analytics Engineers with ownership culture.
• Coach for high performance and meaningful career development.
• Drive engineering execution with clear prioritisation and delivery.
• Champion AI-first engineering culture and automated workflows.
• Define a technical roadmap balancing business priorities with scalability.

Skills

Data engineering leadership

Team coaching

AI mindset

Stakeholder management

Project planning

Communication

Tools

dbt

Databricks

Spark

SQL

Python

Job description

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: MyMode hybrid-working model with Flex, Office-Based, and Remote options *role-dependent All offices are dog-friendly Family Life: For birth parent: 20 weeks of paid parental leave for full-time regular employees For non-birth parents: 12 weeks of paid parental leave for full-time regular employees IVF leave, shared parental leave, and paid emergency parent/carer leave Learn + Grow: Twice-yearly development chats and yearly performance reviews Learning budget Upskilling our employees with company-wide training workshops, materials and resources Your Future: Life Insurance (financial compensation of 3x your salary) Pension matching up to 6% of full base salary with Aviva Depop Extras: In-office Depop Shop (that's free!) and a packing station with free delivery. Special milestones are celebrated with gifts and rewards!