Director of Data

DEPOP; London; On-site; GBP 80,000 - 100,000

Job description

The Role

We are seeking a visionary and technically adept Director of Data Engineering to lead our data engineering initiatives at Depop, as we are at a growth point where we want a step change in the performance of our data. We want insights globalised to the whole business by default, clear and incentivised data ownership and a valuable data catalogue.

We are looking for a customer-centric leader, who measures success in the daily active users of their data products, as well as accuracy and compliance. We want a strategy for low- and high-intent data discovery that prioritises knowledge sharing and resilient data models.

You will lead multiple squads responsible for critical components of our data ecosystem, including experimentation, analytics event logging, our batch data platform on Databricks, Kafka/Avro-based real-time infrastructure, and our analytics engineering platform, which leverages dbt. Your role will be essential in improving self-service capabilities for all users of our data infrastructure, enhancing data quality, and ensuring the usability of our core data sets.

Key Responsibilities:

• Strategic Leadership:
  
  
  • Develop and implement the data engineering strategy, ensuring alignment with Depop's business goals.
  • Lead and mentor 3-4 squads responsible for experimentation, analytics event logging, batch data platform, real-time infrastructure, and analytics engineering.
• Technical Oversight:
  
  
  • Oversee and optimise our data infrastructure, including Databricks (Spark/Spark SQL), Kafka/Avro, and dbt-driven data pipelines.
  • Drive initiatives to improve data quality, usability, and the self-service experience for engineers.
• Cross-Functional Collaboration:
  
  
  • Partner with Data Science, Insights, and Engineering teams to ensure data solutions meet their needs.
  • Advocate for best practices in data governance, security, and compliance.
• Innovation and Continuous Improvement:
  
  
  • Evaluate and implement new technologies to improve data processing, storage, and accessibility.
  • Cultivate a culture of experimentation and continuous improvement within the data engineering team.

Qualifications:

• Experience & Expertise:
  
  
  • Experience in data engineering, within a leadership or managerial role overseeing multiple teams or squads.
  • Strong expertise in data infrastructure, including experience with Databricks, Apache Spark, Spark SQL, Kafka, Avro, and dbt.
  • Demonstrated success in managing data platforms at scale, including both batch processing and real-time streaming architectures.
  • Deep understanding of data warehousing concepts, ETL/ELT processes, and analytics engineering.
  • Strong programming skills, particularly in Python, Scala, or Java, and a solid understanding of cloud platforms (AWS, GCP, or Azure).
• Leadership & Management:
  
  
  • Experience in hiring, mentoring, and developing managers and senior individual contributors.
  • Strong leadership skills with a track record of building and nurturing high-performing, collaborative teams.
  • Ability to inspire and influence teams, driving alignment with organisational goals and encouraging a culture of innovation and continuous improvement.
• Collaboration & Communication:
  
  
  • Experience working closely with data science, analytics, and engineering teams to deliver impactful data solutions.
  • Excellent communication skills, with the ability to influence and align diverse stakeholders.