Principal Data Engineer

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.

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).