AI-Driven Data & Analytics Engineering Manager

Depop Limited - Nova; Greater London; Hybrid; GBP 100,000 - 150,000; Full time

Benefits offered by this job

Health + mental wellbeing PMI

Cycle to Work scheme

Employee Assistance Programme (EAP)

Pension matching

Office dog-friendly

Job summary

Depop is hiring an Engineering Manager – Data & Analytics Engineering to lead a multidisciplinary team delivering scalable data models, pipelines, and semantic layers powering analytics and AI-driven decision making. You will collaborate with Data Science, Platform, and Product leadership to set strategy and ensure high‑quality, trustworthy data assets.

You will drive engineering practices, mentorship, and execution across data quality, testing, CI/CD, and governance while fostering a culture of

Qualifications

• Experience leading data/analytics engineering teams in a consumer tech or marketplace.
• Proven ability to coach and grow engineers and teams.
• Experience applying AI-assisted development, automation, and agentic workflows to improve productivity.

Responsibilities

• Lead Data Engineers and Analytics Engineers to build scalable data pipelines and analytics assets.
• Define technical roadmaps for data and analytics engineering.
• Partner with Data Science, Product, Finance, and Engineering to align priorities and deliver impactful data solutions.
• Promote AI-first engineering practices and governance.

Skills

Data leadership

Coaching

AI-forward mindset

Engineering execution

Stakeholder management

Education

Bachelor's degree in a relevant field

Tools

dbt

Databricks

Spark

SQL

Python

Job description

Depop is hiring an Engineering Manager – Data & Analytics Engineering to lead a multidisciplinary team delivering scalable data models, pipelines, and semantic layers powering analytics and AI-driven decision making. You will collaborate with Data Science, Platform, and Product leadership to set strategy and ensure high‑quality, trustworthy data assets.

You will drive engineering practices, mentorship, and execution across data quality, testing, CI/CD, and governance while fostering a culture of