Senior Manager, Data Engineering & Governance

Dropbox; Seattle (WA); On-site; USD 180,000 - 240,000; Full time

Job summary

Dropbox is seeking a Senior Manager, Data Engineering to lead the team responsible for the reliability, quality, cost, and velocity of our core data platform. This is a hands-on engineering leader who owns the pipelines and data products used by Product, GTM, Finance, and the CTO organization to power decisions.

You will guide ingestion, transformation, orchestration, and serving layers, and build a self-serve analytics substrate that lets partner teams answer questions without bespoke

Qualifications

• 8+ years of data engineering or backend/data infrastructure experience
• 3+ years of direct engineering leadership experience
• Proven track record building and operating large-scale batch and streaming pipelines
• Ownership of data SLAs, observability, lineage, and incident response
• Strong data modeling fundamentals and semantic layer design

Responsibilities

• Data Quality and Observability: establish data quality culture with lineage, freshness monitoring and anomaly detection.
• Self-Serve Platform: build the self-serve analytics substrate to reduce bespoke requests.
• Cost and Efficiency: own unit economics of the data platform, improving compute and storage efficiency.
• Cross-Functional Partnership: collaborate with Data Science, Analytics, Product, and CTO org on data contracts and semantic layer.
• Engineering Culture: implement code review, testing, CI/CD for data, incidents, and postmortems.
• Team Leadership: mentor and grow a high-talent-density team of data engineers.

Skills

Data engineering

Team management

Spark

dbt

Airflow

Databricks

Snowflake

BigQuery

Data pipelines

Observability

Data modeling

Job description

Role Description

We are seeking a Senior Manager, Data Engineering to lead the team responsible for the reliability, quality, cost, and velocity of Dropbox's core data platform. This is a hands-on engineering leader who owns the pipelines and data products that Product, GTM, Finance, and the CTO organization depend on to make decisions.

In this role, you will lead and grow a team of data engineers building and operating our ingestion, transformation, orchestration, and serving layers, as well as the self-serve analytics substrate that lets partner teams answer their own questions without bespoke engineering work.

The ideal candidate is a deeply technical, product-minded engineering leader who can hold a high bar on system reliability and data quality while partnering closely with Data Science, Business Intelligence Engineering, Analytics, and Product to turn fragmented, ticket-driven data work into durable, reusable data products.

Responsibilities

• Data Quality and 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 and 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 and Quality: Demonstrated ownership of data SLAs, observability, lineage, and incident response for business-critical pipelines.
• Systems and Modeling: Strong data modeling fundamentals and the ability to design a semantic layer and data contracts that serve many downstream consumers.