Own and evolve the core data platform powering Plenful’s AI automation engine. Design data models, contracts, and infrastructure for a regulated healthcare domain with 12+ years of backend/data experience.
Salary not listed
Hybrid12+ YOEData Engineering
About the role
What you’ll do
Own the design and evolution of the core data model — domain entities, actions, and the audit trail that governs every automated decision
Build the data layer that AI agents read from and write to: expressive enough that models can reason over operational context, governed enough for healthcare compliance
Define how domain knowledge is structured, versioned, and queried — laying the groundwork for increasingly rich context, serving as the platform matures
Establish data contracts and APIs that give feature teams clean, stable interfaces to build against
Drive platform reliability as we double our customer base and traffic without degrading performance
Set technical direction for the data platform in partnership with the product, and feature team leads
What we’re looking for
Must have
12+ years of professional software engineering experience building backend or data infrastructure in production
Deep expertise in relational databases: schema design, query performance, data modeling tradeoffs
Track record designing and evolving data models in complex, growing systems
Experience operating production systems at scale, including incident response and reliability work
Hands-on coding ability in backend systems (Python-heavy environment, but language agnostic)
Strong reliability instincts: observability, testing, data integrity
Ability to make good decisions with incomplete information — e.g., designing data models when the domain is still being codified, and product requirements are evolving alongside AI capabilities
Nice to have
Experience in healthcare, fintech, or other compliance-heavy infrastructure environments
Background at high-throughput infrastructure companies (Stripe, Brex, Notion, or similar)
Practical understanding of applied AI systems — how models consume structured data, not pure ML research
Comfort in customer-facing technical discussions
Compensation & Benefits
Unlimited PTO
Fully covered health insurance (medical, dental, and vision)
Build and operate production data pipelines, observability tools, and planning systems to maximize utilization, efficiency, and attribution of Anthropic's large-scale multi-cloud accelerator and CPU fleet. Requires strong Python/SQL, cloud operations, and Kubernetes experience in a high-ambiguity environment.
320k – 485k/yr
Hybrid7+ YOEData Engineering
Staff Software Engineer, Communication & Connectivity
AirbnbUnited States
Staff Software Engineer leading design and development of large-scale batch and real-time data pipelines and ML infrastructure to power GenAI/LLM products and features for Airbnb's Messaging, Notifications, and Connectivity organization. Requires 9+ years experience building production ML systems and cross-functional collaboration.
204k – 255k/yr
Remote9+ YOEData Engineering
Staff Software Engineer
RipplingSeattle, WA +2
Build an end-to-end analytics and business intelligence Data Cloud platform at Rippling, replacing customer data lakes, warehouses, and pipelines with integrated ingestion, transformation, lineage, catalogs, and visualization. Develop large-scale data systems using Python, Trino, Iceberg and Temporal; explore ML/LLMs for automated insights.
189k – 315k/yr
Hybrid8+ YOEData Engineering
Staff Data Engineer
CheckrDenver, CO +1
Staff Data Engineer building and evolving Checkr's centralized people data platform and pipelines that power all AI verification products. Requires 10+ years experience with large-scale data platforms, PySpark, Python, SQL, Kafka, Spark, Iceberg and AWS services; will mentor juniors and own architecture.
166k – 230k/yr
Hybrid10+ YOEData Engineering
Staff+ Software Engineer, Databases
AnthropicSan Francisco, CA +2
Build and scale the core database infrastructure powering Claude at Anthropic, including data plane/control plane, data movement (CDC, migrations), and caching systems that support millions of users and frontier AI research across multi-cloud environments. Requires deep expertise in distributed databases and production storage systems.