Is the "Senior Manager, Data Engineering" position at Dropbox fully remote?

Yes, this is a remote position, open to candidates in United States.

What is the salary for the Senior Manager, Data Engineering role at Dropbox?

The listed salary for this position is $209,100 – $282,900 USD, as posted by the employer.

What skills are required for this Senior Manager, Data Engineering position?

Key skills listed for this role include GTM, Adjust, Excel.

How do I apply for the Senior Manager, Data Engineering position at Dropbox?

You can view full details and apply for this position on WFA Digital at https://wfadigital.com/jobs/senior-manager-data-engineering-dropbox-4640.

Senior Manager, Data Engineering

Dropbox·Remote(Remote - US: Select locations)·$209,100 – $282,900 USD

Software Development

GTMAdjustExcel

WFA Digital Insight

Dropbox’s data platform powers everything from product decisions to finance forecasts, and this senior manager sits at the heart of that engine. The role is hands‑on, meaning the leader will still write code while scaling a team that builds ingestion pipelines, transformation logic and the self‑serve analytics layer that lets other groups answer questions without custom engineering. What sets this position apart is the explicit focus on data quality observability and unit economics – you’ll be expected to balance freshness, lineage and anomaly detection with cost‑efficient compute. Collaboration is baked in; you’ll work directly with GTM, finance, data science and the CTO organization to define data contracts that make downstream work trustworthy. Candidates who thrive on turning fragmented data work into reusable products will find this environment uniquely rewarding.

Job Description

Role Description

We are seeking a Senior Manager, Data Engineering to lead the team responsible for Dropbox’s underlying data foundations that power our business as a whole. 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.

Our Engineering Career Framework is viewable by anyone outside the company and describes what’s expected for our engineers at each of our career levels. Check out our blog post on this topic and more here.

Responsibilities

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.

Preferred Qualifications

Platform / Self-Serve Experience: Track record building self-serve data or analytics platforms that reduced bespoke request volume and increased partner autonomy.
AI-Forward Engineering: Experience integrating AI coding tools and LLM-based tooling into the engineering workflow, with a measured approach to impact and guardrails.
Cost Discipline: Demonstrated success improving compute/storage unit economics without regressing reliability.
Familiarity with modern data governance, privacy, and access-control practices.
Experience operating in a pod or embedded model serving multiple business partners.

Durable Skills

AI fluency means using these tools to amplify human judgment, not replace it. We believe people with these skills will thrive as work and technology continue to evolve:

Awareness: Understand yourself and others.
Judgment: Evaluate information and make decisions in complex situations.
Adaptability: Learn, adjust, and stay effective through change.
Connection: Communicate, collaborate, and build trust.

To learn more about why these skills matter and what the data shows about thriving through change, read this blog post from our Chief People Officer, Melanie Rosenwasser.

Compensation

Canada Pay Range

$209,100—$282,900 CAD

How to Stand Out

Highlight concrete examples where you built or improved data pipelines that reduced latency or cost.
Showcase any experience you have with GTM, Adjust or large‑scale Excel reporting in your resume or portfolio.
Prepare to discuss your approach to data quality observability, including tools you’ve used for lineage and anomaly detection.
During interviews, emphasize how you balance engineering rigor with business outcomes and cost efficiency.
If you have used AI coding assistants, be ready to talk about the safeguards you put in place.
Ask about the current self‑serve analytics substrate roadmap to demonstrate genuine interest.
Remember to negotiate remote‑work stipends or home‑office equipment if they are not explicitly listed.

This is a remote position listed on WFA Digital, the platform for professionals who work from anywhere. Browse more remote jobs across all categories.