VP of Data Platform & Architecture

What is a day in the life of a Vice President of Data Platform & Architecture?

• Lead the data organization through directors and managers, focusing on the strategic evolution of data engineering and platform architecture.

• Architect and scale distributed systems capable of processing petabyte-scale datasets and hundreds of billions of events.

• Collaborate with cross-functional teams to drive a company-wide data vision and operating model aligned with corporate goals.

• Obsess over the "developer experience," building self-service capabilities and abstractions that reduce time-to-delivery from days to minutes.

• Manage the performance and cost efficiency of the platform, including transitions toward open-source technologies like Airflow, Debezium, Iceberg, Trino, and Spark.

• Mentor international engineering teams, establishing high-level frameworks for technical excellence and operational rigor.

What will I need to thrive in this role?

• Scale Systems Expertise: Deep experience architecting and operating large-scale distributed data platforms at a multi-petabyte scale on cloud services (AWS preferred).

• Platform-First Mindset: Proven ability to build internal developer frameworks and abstractions rather than just managing pipelines.

• Technical Depth: Strong background in software engineering and distributed systems design, specifically with batch and real-time processing.

• Global Leadership: Experience leading and scaling international engineering organizations of 50+ professionals.

• Open-Source Fluency: Familiarity with modern open-source stacks (e.g., Airflow, Debezium, Kafka, Spark, Clickhouse) and the trade-offs involved in platform architecture.

• Data Fanaticism: A relentless focus on using data to improve system performance, cost-efficiency, and developer velocity.

What does high performance look like?

• Project Execution: The data platform evolves into an open-source, scalable, self-service environment that is fully adopted by engineering teams across the company.

• System Impact: Measurable improvements in system latency, throughput, and cost-efficiency for production-critical workloads.

• Team Impact: Measurable increase in developer velocity across the organization, driven by the removal of architectural friction and support of AI tools.

What is my potential for career growth?

• Direct influence on the technical trajectory of a global ecommerce leader.

• Opportunity to lead a high-stakes transition from commercial to open-source platform architecture.

• Executive-level networking and strategic partnership with the broader Engineering and Product leadership.

• Role as the primary technical authority on scalability and distributed systems for the entire organization.

What does success look like in the first 30, 60, 90 days?

• 30 Days: Complete a comprehensive audit of the current architecture, identify friction points in developer velocity, and build relationships with international team leads.

• 60 Days: Refine the roadmap for platform abstractions and contribute to the transition plan for key open-source components.

• 90 Days: Demonstrate early wins in system performance or cost-efficiency.

What is the team like?

• This role reports directly to the Chief Technology Officer (CTO).

• You will be joining a growing team of 50+ professionals across global locations.

• In this role, you will collaborate closely with Directors of Data Engineering, SRE Leads, and Product Managers as well as other departments including Engineering, Security, and Sales.

• This position is mentored by the CTO.

What is the hiring process?

• Recruiter Interview

• Technical/Architectural Deep Dive Interview

• Interview with Engineering Leadership

• Reference Checks

• Executive Review

• Offer

How can I stand out as an applicant?

• The "Nice-to-Haves": Experience in building developer tools, familiarity with Clickhouse, and a history of contributing/managing significant open-source data projects

• Stand-Out Tips:

• Be prepared to discuss and articulate trade-offs across database types, streaming platforms, query engines, and orchestration tools - including commercial vs. open-source considerations at scale.

• Highlight specific instances where you improved developer velocity by introducing platform abstractions.

• Demonstrate your ability to lead and unify engineering teams across different time zones and cultures.