Role
We're building a Data Quality, Observability & Governance Team to improve the reliability, trust, and compliance of Depop's data ecosystem.
As a Staff Data Engineer in this team, you'll lead the design and implementation of frameworks, tools, and processes that strengthen our data foundations - ensuring our data is accurate, observable, and compliant.
Your mission will be to reduce the mean time to detection and resolution of data incidents, by establishing data contracts between producers and consumers, developing robust data observability systems, and embedding governance and GDPR compliance principles across the data lifecycle.
You'll collaborate with product engineering, data platform, analytics, and legal teams to build confidence in data as a product - one that's reliable, auditable, and actionable.
Responsibilities
- Define and execute the vision for Depop's data quality, observability, and governance frameworks.
- Establish data contracts between producers and consumers to ensure schema integrity and data reliability.
- Develop and maintain systems that detect, alert, and resolve data quality issues with minimal latency.
- Build automation and tooling to reduce MTTD (Mean Time to Detection) and MTTR (Mean Time to Resolution) for data incidents.
- Partner with data platform engineers to integrate observability at every layer - ingestion, transformation, and consumption.
- Lead GDPR and privacy‑by‑design initiatives, ensuring compliance and traceability across all datasets.
- Define standards for metadata management, lineage tracking, and access control.
- Collaborate with analytics and product teams to ensure data definitions and quality metrics are consistent across domains.
- Mentor engineers and analysts, fostering a culture of data stewardship and accountability.
- Continuously improve data governance maturity through automation, documentation, and measurable quality KPIs.
Qualifications
- Proven experience as a Staff Data Engineer or in an equivalent technical leadership role in data quality, observability, or governance.
- Deep knowledge of data observability frameworks (Monte Carlo, Soda, or equivalent) and data validation tools (Great Expectations, DBT tests, etc.).
- Deep understanding of data‑as‑a‑product principles and experience applying them to improve data reliability and ownership.
- Experience designing and enforcing data contracts and quality SLAs in distributed data ecosystems.
- Proficiency in Python, Java, or Scala, and experience building pipelines with Databricks, Spark, or Kafka.
- Strong understanding of data governance principles, privacy regulations (GDPR, CCPA), and secure data handling practices.
- Familiarity with metadata management and data catalog tools (e.g. DataHub, Collibra, etc.).
- Demonstrated success improving data reliability and observability in large‑scale data platforms.
- Excellent communication and stakeholder management skills; you can bridge technical depth with operational impact.
Bonus Points
- Experience implementing automated compliance monitoring or policy‑as‑code systems.
- Familiarity with real‑time anomaly detection for data pipelines.
- Experience contributing to or leading cross‑functional data reliability initiatives.
- Prior experience in consumer or marketplace platforms.
- Passion for data as a product - building reliable, observable, and compliant data systems that teams love to use.
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