Senior Manager of Data Engineering

Join Dropbox as a Senior Manager of Data Engineering, where you will establish a rigorous data quality culture, lead the engineering of a self-serve analytics platform, and drive measurable improvements in cost and efficiency. You will partner with various teams, establish engineering practices, and lead a high-talent-density team of data engineers. Enjoy benefits such as on-site fitness classes, medical coverage, a 401K retirement plan, paid time off, and more.

• Establish and enforce a rigorous data quality culture, including lineage, freshness monitoring, anomaly detection, and quality metrics.
• Lead the engineering of the self-serve analytics substrate, reducing bespoke request volume and increasing partner-team autonomy.
• Own the unit economics of the data platform, driving measurable improvements in compute and storage efficiency without sacrificing reliability.
• Stakeholder Management: Excellent communication and the ability to align engineering, data science, analytics, and business partners around shared reliability and quality goals
• Reliability & Quality: Demonstrated ownership of data SLAs, observability, lineage, and incident response for business-critical pipelines
• Deep Technical Expertise: Proven track record building and operating large-scale batch and streaming pipelines (e.g., Spark, dbt, Airflow/orchestration) on a modern lakehouse or warehouse stack (e.g., Databricks, Snowflake, BigQuery)
• 8+ years of data engineering or backend/data infrastructure experience with increasing scope, ideally in high-scale environments
• Platform / Self-Serve Experience: Track record building self-serve data or analytics platforms that reduced bespoke request volume and increased partner autonomy
• 3+ years of experience directly managing and growing engineering teams, including hiring, coaching, performance management, and team design
• Systems & Modeling: Strong data modeling fundamentals and the ability to design a semantic layer and data contracts that serve many downstream consumers
• Cost Discipline: Demonstrated success improving compute/storage unit economics without regressing reliability
• AI-Forward Engineering: Experience integrating AI coding tools and LLM-based tooling into the engineering workflow, with a measured approach to impact and guardrails
• Experience operating in a pod or embedded model serving multiple business partners
• Familiarity with modern data governance, privacy, and access-control practices