Senior Manager, Data Engineering

🕒 5 days ago

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Dropbox

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2007

💼 Consulting

🏭 Manufacturing

📦 Logistics

Consulting • Manufacturing • Logistics

Dropbox is a cloud-based service that provides tools for storing, sharing, and accessing files across devices. It offers features such as document sharing, video review, automatic backups, and AI-driven scheduling. Dropbox also provides solutions for different sectors like teams, sales, marketing, and education, and industries including construction, media, technology, and manufacturing. With a focus on security, Dropbox ensures files are encrypted and protected against tampering. It offers integrations with various productivity tools and is trusted by major companies for efficient file management and collaboration.

📋 Description

• Lead and grow a team of data engineers building and operating ingestion, transformation, orchestration, serving, and self-serve analytics layers • Establish data quality practices covering lineage, freshness monitoring, anomaly detection, and quality metrics • Lead the self-serve analytics platform to reduce bespoke requests and increase partner-team autonomy • Own data-platform compute and storage efficiency while improving reliability • Partner with Data Science, BIE, Analytics, Product, Data Platform, and the CTO organization on semantic layers, modeling standards, and data contracts • Establish engineering practices including code review, testing, CI/CD for data, incident response, and postmortems • Champion measured use of AI coding tools to improve engineering productivity • Mentor and develop data engineers while fostering ownership, technical excellence, psychological safety, and continuous learning

🎯 Requirements

• 8+ years of data engineering or backend/data infrastructure experience, ideally in high-scale environments • 3+ years directly managing and growing engineering teams, including hiring, coaching, performance management, and team design • Proven experience building and operating large-scale batch and streaming pipelines • Experience with Spark, dbt, and Airflow/orchestration • Experience with modern lakehouse or warehouse platforms such as Databricks, Snowflake, or BigQuery • Ownership of data SLAs, observability, lineage, and incident response for business-critical pipelines • Strong data modeling fundamentals • Ability to design semantic layers and data contracts for downstream consumers • Excellent communication and stakeholder-alignment skills across engineering, data science, analytics, and business partners