What You’ll Do

• Lead the design and build-out of cross-product data services for multiple product lines from one governed data plane.
• Define the “customer data plane” model: canonical customer identifiers, shared dimensions, and consistent facts used across products.
• Build and operationalize ingestion patterns for batch, streaming, and event data, with repeatable onboarding for new sources.
• Own the operational playbook for data reliability: data contracts, quality checks, lineage, monitoring, and incident response.
• Implement and run access methods that make data usable: curated datasets, secure query interfaces, and product-ready data APIs where needed.
• Productize customer-facing data products (datasets, metrics, exports, and feeds) with versioning, documentation, and clear ownership.
• Design data models that fit both operational systems (RDS) and analytics stores (columnar/OLAP), including performance and cost tuning.
• Ensure data products also power ML workflows: trusted training datasets, feature-ready outputs, and consistent definitions for decision-making.
• Enable AI automation by delivering reliable, low-latency, governed data products that can be used safely in automated workflows.
• Partner closely with product, engineering, and security stakeholders to align data products to roadmap priorities and customer outcomes.
• Raise the technical bar through architecture reviews, standards, and mentoring—while staying hands-on in key systems.
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Required

• Significant experience building and operating production data platforms at scale, including on-call and operational ownership.
• Strong SQL skills and strong Python skills, used to build pipelines, services, and automation.
• Hands-on experience running cloud systems on AWS and Google Cloud (IaaS level: compute, storage, networking, IAM).
• Practical experience with both operational databases (RDS-style) and analytics stores (columnar/OLAP), including performance tuning.
• Strong data modeling ability, including schema evolution, conformed dimensions, and “one source of truth” metric definitions.
• Track record of delivering data products that other teams or customers depend on, with clear contracts and reliability expectations.
• Ability to make sound engineering tradeoffs across latency, accuracy, cost, and security without creating brittle complexity.
• Experience with lakehouse patterns and open table formats (or similar), including governance and table maintenance.
• Experience with orchestration and streaming systems used in production (batch + real-time), and managing backfills safely.
• Familiarity with ML data needs (training/serving splits, feature-ready datasets, evaluation datasets) and AI-adjacent workflows.
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