Engineering Manager - Data Quality & Governance

Engineering Manager - Data Quality & Governance

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Company Description Depop is the community‑powered circular fashion marketplace where anyone can buy, sell and discover desirable secondhand fashion. With a community of over 35 million users, Depop is on a mission to make fashion circular, redefining fashion consumption. Founded in 2011, the company is headquartered in London, with offices in New York and Manchester, and in 2021 became a wholly‑owned subsidiary of Etsy.

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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. 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 . For any other non‑disability related questions, please reach out to our Talent Partners.

We’re building a Data Quality, Observability & Governance Team to make Depop’s data more reliable, trustworthy, and compliant. As an Engineering Manager (Tech Lead Manager) for this team, you’ll lead a group of engineers focused on improving data reliability, reducing time to detect and resolve data incidents, and driving accountability across data producers and consumers. You’ll provide both technical leadership and people management, helping define and execute the strategy for data observability, data contracts, and governance frameworks — ensuring our data is treated as a product: reliable, discoverable, high‑quality, and built with clear ownership and accountability.

Your team’s mission is to improve the mean time to detection and resolution (MTTD/MTTR) for data incidents, establish data contracts between producers and consumers, and embed GDPR and privacy‑by‑design principles into our data stack.

• Lead and grow a multidisciplinary team of data and backend engineers focused on data quality, observability, and governance.
• Drive the technical direction for data observability tooling, incident management automation, and quality frameworks.
• Champion a “data as a product” culture, ensuring data producers own the quality, discoverability, and usability of their datasets.
• Define and roll out data ownership models, quality SLAs, and contracts aligned with product thinking.
• Collaborate with data platform, analytics, machine learning and product engineering teams to embed observability and validation throughout our data lifecycle.
• Partner closely with legal, compliance, and security teams to ensure GDPR and privacy‑by‑design are integrated into all systems.
• Define data reliability KPIs and build monitoring and alerting systems for proactive incident detection.
• Recruit, mentor, and develop engineers; foster a team culture centred on accountability, learning, and craftsmanship.
• Balance hands‑on technical work with coaching and strategic planning – setting clear goals, metrics, and delivery outcomes.
• Represent the team in cross‑functional forums, influencing technical direction across Depop’s broader data ecosystem.
• Continuously improve team operations, emphasising automation, transparency, and measurable impact on data reliability.

• Proven experience as an Engineering Manager or Tech Lead Manager leading data or platform teams.
• Strong background in data engineering, observability, and distributed systems – ideally with prior hands‑on experience in data infrastructure, reliability, or governance.
• Expertise in building or managing systems leveraging Databricks, Spark, Kafka, or Airflow, with a strong grasp of modern data stacks.
• Familiarity with data…