Senior Manager, Data Engineering

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🔥 5 minutes ago

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Logo of Dropbox

Dropbox

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

• Data Quality & Observability: Establish and enforce a rigorous data quality culture: lineage, freshness monitoring, anomaly detection, and outcome-oriented, gaming-resistant quality metrics. • Self-Serve Platform: Lead the engineering of the self-serve analytics substrate, reducing bespoke request volume and increasing partner-team autonomy. • Cost & Efficiency: Own the unit economics of the data platform — compute and storage efficiency — and drive measurable improvements without sacrificing reliability. • Cross-Functional Partnership: Partner deeply with Data Science, BIE, Analytics, Product, Data Platform, and the CTO org to define the semantic layer, modeling standards, and data contracts that make downstream work trustworthy and fast. • Engineering Culture: Establish rigorous engineering practices — code review, testing, CI/CD for data, incident response, and postmortems — and champion the effective, measured use of AI coding tools to improve engineering productivity. • Team Leadership: Lead, mentor, and grow a high-talent-density team of data engineers, fostering a culture of ownership, technical excellence, psychological safety, and continuous learning.

🎯 Requirements

• 8+ years of data engineering or backend/data infrastructure experience with increasing scope, ideally in high-scale environments. • 3+ years of experience directly managing and growing engineering teams, including hiring, coaching, performance management, and team design. • 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). • Reliability & Quality: Demonstrated ownership of data SLAs, observability, lineage, and incident response for business-critical pipelines. • Systems & Modeling: Strong data modeling fundamentals and the ability to design a semantic layer and data contracts that serve many downstream consumers. • Stakeholder Management: Excellent communication and the ability to align engineering, data science, analytics, and business partners around shared reliability and quality goals.

🏖️ Benefits

• This role is not available in Zone 1 • US Zone 2: $202,700 - $274,300 USD • US Zone 3: $180,200 - $243,800 USD

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