Senior Technical Product Manager I (Data Platform)

Zip Co Limited

17h • Remote

About The Position

The Senior Technical Product Manager (Data Platform) owns the strategy, roadmap, and execution of Zip’s foundational data platform. This is a platform-focused role, not a business use-case PM position. You will be responsible for building and scaling reliable, compliant, and AI-ready data infrastructure that powers analytics, risk modeling, fraud detection, regulatory reporting, and production AI capabilities across the company. This role ensures our data assets are trusted, governed, scalable, cost-efficient, and accessible through self-serve capabilities—while meeting the regulatory standards required in fintech.
You will partner closely with Data Engineering, ML Engineering, Agentic AI, Enterprise Architecture, Risk, Compliance, and Product teams to define platform priorities and deliver durable infrastructure that scales with the business.
Interesting problems you’ll get to solve
Data Platform Strategy & Roadmap
Define and execute the multi-year vision and roadmap for Zip’s enterprise Data Platform.
Own foundational capabilities, including ingestion, storage, transformation (ETL/ELT), orchestration, metadata management, and access control.
Ensure alignment with company-wide analytics, AI, and regulatory priorities.
Balance speed, reliability, scalability, compliance, and cost in roadmap decisions.
Data Governance, Controls & Compliance
Establish and enforce enterprise data governance frameworks.
Implement and maintain SOX-compliant controls across critical datasets and reporting workflows.
Define and operationalize data contracts between producers and consumers.
Ensure lineage tracking, auditability, and regulatory reporting integrity.
Partner with Risk and Compliance to align with data privacy and regulatory requirements (e.g., SOX, KYC, AML, GDPR where applicable).
Reliability, Quality & Platform Health
Define data quality standards, SLAs, freshness requirements, and certification processes.
Establish measurable platform health metrics (uptime, quality scores, adoption, cost efficiency).
Drive proactive monitoring and observability for pipelines and datasets.
Reduce friction for AI and analytics teams by improving data discoverability and trust.
AI & Advanced Analytics Enablement
Ensure the data platform supports production-grade AI/ML workloads.
Enable reusable, well-governed data foundations for feature engineering and experimentation.
Partner with ML Engineering to ensure model-ready, high-quality datasets.
Transition the organization from AI experimentation to scalable, production-ready AI capabilities.
Self-Service & Adoption
Enable self-service analytics capabilities while maintaining governance guardrails.
Improve discoverability and usability of certified datasets.
Drive adoption of standardized, governed data assets across teams.
Cost Optimization & Scalability
Partner with engineering to optimize warehouse, compute, and storage costs.
Drive architectural decisions that scale efficiently as data volume and AI usage grow.
Continuously evaluate tradeoffs between performance, reliability, and cost.

Requirements

• 7–10+ years in Product Management or equivalent experience, with a focus on data platforms, data engineering, or analytics infrastructure
• Strong understanding of modern data architectures (lakehouse, warehouse, ELT/ETL, streaming/event-driven systems)
• Experience defining and managing data contracts, governance frameworks, and quality standards
• Experience operating in regulated environments (SOX compliance strongly preferred; fintech or financial services experience a plus)
• Deep familiarity with data platforms such as Snowflake, BigQuery, Redshift, Kafka/Kinesis, or similar ecosystems
• Strong ability to translate complex technical capabilities into measurable business impact
• Demonstrated experience driving cross-functional alignment across engineering, compliance, and business teams
• Comfortable making principled tradeoffs between speed, reliability, compliance, and cost

Nice To Haves

• Experience enabling AI/ML workloads through reusable, governed data infrastructure
• Systems thinker who understands interconnected platform tradeoffs
• Strong technical fluency with data engineering patterns and architecture decisions
• Proven ability to drive clarity in ambiguous, evolving environments
• Experience optimizing infrastructure cost and operational efficiency at scale
• Ability to influence without authority and lead through alignment.

Responsibilities

• Define and execute the multi-year vision and roadmap for Zip’s enterprise Data Platform.
• Own foundational capabilities, including ingestion, storage, transformation (ETL/ELT), orchestration, metadata management, and access control.
• Ensure alignment with company-wide analytics, AI, and regulatory priorities.
• Balance speed, reliability, scalability, compliance, and cost in roadmap decisions.
• Establish and enforce enterprise data governance frameworks.
• Implement and maintain SOX-compliant controls across critical datasets and reporting workflows.
• Define and operationalize data contracts between producers and consumers.
• Ensure lineage tracking, auditability, and regulatory reporting integrity.
• Partner with Risk and Compliance to align with data privacy and regulatory requirements (e.g., SOX, KYC, AML, GDPR where applicable).
• Define data quality standards, SLAs, freshness requirements, and certification processes.
• Establish measurable platform health metrics (uptime, quality scores, adoption, cost efficiency).
• Drive proactive monitoring and observability for pipelines and datasets.
• Reduce friction for AI and analytics teams by improving data discoverability and trust.
• Ensure the data platform supports production-grade AI/ML workloads.
• Enable reusable, well-governed data foundations for feature engineering and experimentation.
• Partner with ML Engineering to ensure model-ready, high-quality datasets.
• Transition the organization from AI experimentation to scalable, production-ready AI capabilities.
• Enable self-service analytics capabilities while maintaining governance guardrails.
• Improve discoverability and usability of certified datasets.
• Drive adoption of standardized, governed data assets across teams.
• Partner with engineering to optimize warehouse, compute, and storage costs.
• Drive architectural decisions that scale efficiently as data volume and AI usage grow.
• Continuously evaluate tradeoffs between performance, reliability, and cost.

Benefits

• Flexible working culture
• Incentive programs
• Unlimited PTO
• Generous paid parental leave
• Leading family support policies
• Company-sponsored 401k match
• Learning and wellness subscription stipend
• Beautiful Union Square office with a casual dress code
• Industry-leading, employer-sponsored insurance for you and your dependents, with several 100% Zip-covered choices available

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed