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We are building an AI-native Data Cloud and need a VP of Engineering who owns architecture, execution, and business outcomes end-to-end. The buck stops with this role for platform reliability, developer experience, and Data Cloud adoption.

DigitalOcean’s Data Cloud will power AI applications end-to-end—from data ingestion to real-time inference—enabling developers to build production-grade RAG systems, AI agents, and analytics workflows on a single, integrated platform. This role is responsible for delivering that platform by leveraging and extending DigitalOcean’s existing infrastructure, PaaS, and AI capabilities.

This is not a traditional data platform role. You will define and execute the strategy to move DigitalOcean up the stack—from infrastructure provider to a differentiated AI application and inference platform. Success will be measured through roadmap velocity, system reliability, developer adoption, and measurable business impact.

We are looking for a leader who translates strategy into shipped systems, with clear accountability for outcomes.

What You’ll Do:

Data Cloud Platform

Architecture and delivery of a unified platform spanning:

• Ingestion (connectors, CDC, streaming)
• Storage (object, OLAP, vector, graph)
• Processing (ELT pipelines, real-time and batch)
• Query & analytics (SQL, hybrid search, RAG)
• Governance (catalog, lineage, versioning, access control)

AI-Native Capabilities

• Knowledgebase-as-a-Service (RAG pipelines)
• Vector and hybrid retrieval systems
• Feature and embedding pipelines
• Real-time inference data paths

Product & Execution

• Deliver GA-grade services with strong SLAs, cost efficiency, and simplicity
• Drive roadmap aligned to Digital Native Enterprise (DNE) customers
• Establish and manage clear metrics: adoption, retention, ARPU, performance, reliability

Organizational Leadership

• Build and scale a high-performing global organization (US and India)
• Implement single-threaded ownership (STO) across services
• Partner deeply with Product, GTM, and Platform Engineering

What Success Looks Like (12 months)

• Launch a cohesive Data Cloud platform—not a collection of fragmented services
• Achieve meaningful adoption across AI-native workloads (RAG, inference, analytics)
• Reduce developer time-to-value from weeks to hours
• Establish clear differentiation versus hyperscalers on simplicity and cost

What You’ll Bring:

• Proven track record of building and operating cloud-scale data platforms (OLAP, lakehouse, streaming, or ML systems), with strong architectural judgment across storage, processing, and query layers
• Experience integrating open-source ecosystems (e.g., Spark, Airflow, dbt) into managed cloud services at scale
• Experience with AI/ML data pipelines (feature stores, embeddings, RAG)
• Demonstrated ability to operate in ambiguity, make high-quality decisions with incomplete data, and drive multi-quarter execution with measurable outcomes
• Proven experience scaling engineering organizations and delivering under real-world constraints

Critical requirement:You have personally driven complex distributed systems from concept to production—not just managed teams that did.

Why This Role Matters

The Data Cloud is the control plane for AI applications. If DigitalOcean gets this right, it becomes the platform where developers build and scale intelligent applications—not just where infrastructure runs. If not, it remains an interchangeable infrastructure.

Compensation Range: 

• $279,200.00 - $349,000

*This is a hybrid role

JR: 2026-7683

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