VP of Data Platform & Architecture
Indexed description
What is a day in the life of a Vice President of Data Platform & Architecture?
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Lead the data organization through directors and managers, focusing on the strategic evolution of data engineering and platform architecture.
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Architect and scale distributed systems capable of processing petabyte-scale datasets and hundreds of billions of events.
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Collaborate with cross-functional teams to drive a company-wide data vision and operating model aligned with corporate goals.
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Obsess over the "developer experience," building self-service capabilities and abstractions that reduce time-to-delivery from days to minutes.
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Manage the performance and cost efficiency of the platform, including transitions toward open-source technologies like Airflow, Debezium, Iceberg, Trino, and Spark.
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Mentor international engineering teams, establishing high-level frameworks for technical excellence and operational rigor.
What will I need to thrive in this role?
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Scale Systems Expertise: Deep experience architecting and operating large-scale distributed data platforms at a multi-petabyte scale on cloud services (AWS preferred).
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Platform-First Mindset: Proven ability to build internal developer frameworks and abstractions rather than just managing pipelines.
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Technical Depth: Strong background in software engineering and distributed systems design, specifically with batch and real-time processing.
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Global Leadership: Experience leading and scaling international engineering organizations of 50+ professionals.
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Open-Source Fluency: Familiarity with modern open-source stacks (e.g., Airflow, Debezium, Kafka, Spark, Clickhouse) and the trade-offs involved in platform architecture.
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Data Fanaticism: A relentless focus on using data to improve system performance, cost-efficiency, and developer velocity.
What does high performance look like?
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Project Execution: The data platform evolves into an open-source, scalable, self-service environment that is fully adopted by engineering teams across the company.
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System Impact: Measurable improvements in system latency, throughput, and cost-efficiency for production-critical workloads.
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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?
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Direct influence on the technical trajectory of a global ecommerce leader.
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Opportunity to lead a high-stakes transition from commercial to open-source platform architecture.
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Executive-level networking and strategic partnership with the broader Engineering and Product leadership.
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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?
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30 Days: Complete a comprehensive audit of the current architecture, identify friction points in developer velocity, and build relationships with international team leads.
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60 Days: Refine the roadmap for platform abstractions and contribute to the transition plan for key open-source components.
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90 Days: Demonstrate early wins in system performance or cost-efficiency.
What is the team like?
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This role reports directly to the Chief Technology Officer (CTO).
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You will be joining a growing team of 50+ professionals across global locations.
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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.
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This position is mentored by the CTO.
What is the hiring process?
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Recruiter Interview
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Technical/Architectural Deep Dive Interview
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Interview with Engineering Leadership
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Reference Checks
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Executive Review
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Offer
How can I stand out as an applicant?
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The "Nice-to-Haves": Experience in building developer tools, familiarity with Clickhouse, and a history of contributing/managing significant open-source data projects
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Stand-Out Tips:
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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.
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Highlight specific instances where you improved developer velocity by introducing platform abstractions.
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Demonstrate your ability to lead and unify engineering teams across different time zones and cultures.
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