This is not a maintenance role. We are modernizing our analytics platform, upgrading orchestration infrastructure, building shared and reusable data models with conformed dimensions, establishing a certified metrics framework, and laying the foundation for AI-native data development. You will partner closely with Data Science, Data Infrastructure, Product Engineering, and Business Intelligence teams to make this happen.

You will play a crucial role in establishing analytics engineering standards, designing scalable data models, and driving cross-functional alignment on data governance. You will get substantial exposure to senior leadership, shape the technical direction of analytics infrastructure at Dropbox, and directly influence how data powers product and business decisions.

Responsibilities:

• Lead the design and implementation of shared, reusable data models, defining shared fact tables, conformed dimensions, and a semantic/metrics layer that serves as the single source of truth across analytics functions

• Drive standardization of data engineering practices across ADE and functional analytics teams, including pipeline patterns, CI/CD workflows, naming conventions, and data modeling standards

• Partner with Data Infrastructure to modernize orchestration, improve pipeline decomposition, and establish secure dev/test environments with production data access

• Architect and implement a shift-left data governance strategy, working with upstream data producers to establish data contracts, SLOs, and code-enforced quality gates that catch issues before production

• Collaborate with Data Science leads and Product Management to translate metric definitions into reliable, certified data pipelines that power executive dashboards, WBR reporting, and growth measurement

• Reduce operational burden by improving pipeline granularity, observability, and failure recovery, establishing runbooks and alerting standards that make on-call sustainable

• Evaluate and integrate AI-native tooling into the data development lifecycle, enabling conversational data exploration with guardrails and AI-assisted pipeline development

Requirements:

• BS degree in Computer Science or related technical field, or equivalent technical experience

• 12+ years of experience in data engineering or analytics engineering with increasing scope and technical leadership

• 12+ years of SQL experience, including complex analytical queries, window functions, and performance optimization at scale (Spark SQL)

• 8+ years of Python development experience, including building and maintaining production data pipelines

• Deep expertise in dimensional data modeling, schema design, and scalable data architecture, with hands-on experience building shared data models across multiple business domains

• Strong experience with orchestration tools (Airflow strongly preferred) and dbt, including pipeline design, scheduling strategies, and failure recovery patterns

Preferred Qualifications:

• Experience with Databricks (Unity Catalog, Delta Lake) and modern lakehouse architectures

• Experience leading orchestration or platform modernization efforts at scale

• Familiarity with data governance and observability tools such as Atlan, Monte Carlo, Great Expectations, or similar

• Experience building or contributing to a metrics/semantic layer (dbt MetricFlow, Databricks Metric Views, or equivalent)

• Track record of establishing data engineering standards and best practices in a federated analytics organization

Compensation:

US Zone 2 $198,900-$269,100 USD

US Zone 3 $176,800-$239,200 USD

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The MaaS Data Science team is central to all money movements, embedded finance, and platform solutions for our biggest and most complex customers. The two roles available are in Embedded Finance (Capital, Issuing) and Connect (Stripe's solution and growth suite for platforms and marketplaces).

The Growth Data Science team helps businesses on Stripe get started both quickly and effectively. We work closely with Growth product and engineering leads to optimize every step of the user journey, from awareness and acquisition, through product adoption, to usage growth and retention.

Responsibilities:

• Drive the roadmap and priorities for your team, and work with many Stripe leaders across the company to enhance our ability to be data driven.
• Collaborate with stakeholders across the organization such as engineering, analytics, operations, finance, and marketing.
• Lead and manage processes to help the team do its best work and engage effectively with the rest of Stripe.
• Manage a high-performing team of data scientists, supporting them to achieve a high level of technical excellence and advance in their careers.
• Recruit and onboard great data scientists, in collaboration with Stripe’s recruiting team.
• Contribute to broad data science initiatives as a member of Stripe’s data science management team.

Requirements:

• PhD or MS or BS in a quantitative field (e.g., Statistics, Operations Research, Economics, Computer Science, Engineering).
• At least 3 years of direct management experience leading data science or ML teams, and 10 years of overall data science experience.
• Demonstrated expertise in designing metrics and guiding business decisions with data.
• Technical expertise to drive clarity with staff and senior scientists about architecture and strategic modeling decisions.
• Managed teams that have built and shipped machine learning systems and data products at scale, and have hands-on experience with challenging problems.
• Work very well cross-functionally, and are able to think rigorously and make hard decisions and tradeoffs.
• Clear and persuasive communication skills in writing and verbally.
• Thrive on a high level of autonomy and responsibility.
• Foster a healthy, inclusive, challenging, and supportive work environment.

Preferred Requirements:

• Comfortable working with geographically distributed teams.
• Expertise in time series forecasting, predictive modeling, or optimization.
• Expertise in data design and building scalable data architectures.

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