Principal Software Engineer

Plenful•San Francisco, CA

17d•Hybrid

About The Position

We are seeking a Principal Software Engineer to lead the design and development of core data systems that power a scalable automation platform. This role focuses on building foundational infrastructure for managing complex workflows, system state, and historical data in a high-reliability environment. This is a highly technical, hands-on role centered on system architecture and platform design. You will create durable, extensible data models and services that enable other engineering teams to build efficiently and safely on top of a shared foundation.

Requirements

12+ years owning data architecture in large-scale production systems.
Deep relational database and distributed systems expertise.
Strong experience evolving schemas in complex, regulated domains.
Clear judgment on normalization vs. denormalization, performance tradeoffs, and system boundaries.
Experience handling mission-critical incidents in production.
Comfort operating in ambiguous, fuzzy problem spaces.
Hands-on coding ability in a Python-heavy backend environment.
Strong reliability instincts, including observability, testing, and QA rigor.

Nice To Haves

Experience in healthcare, fintech, or infrastructure environments.
Exposure to or experience with modern, scaled, high-throughput infrastructure environments such as Stripe, Brex, or Notion.
Practical understanding of applied AI systems (not pure ML research).
Comfort in customer-facing technical discussions.

Responsibilities

Define and evolve core data models for workflows, system state, actions, and outcomes
Design scalable, maintainable abstractions across platform services
Establish clear boundaries between systems and services to support team autonomy
Architect systems for managing workflow state and historical data across distributed infrastructure
Ensure high performance, reliability, and scalability for high-throughput systems
Make informed tradeoffs in data modeling, indexing, and access patterns
Design systems that support traceability and observability of system behavior
Ensure data models and storage systems support auditing, versioning, and evolution over time
Build mechanisms to maintain data consistency and integrity
Establish best practices for schema management, testing, and data quality
Improve system reliability and reduce operational overhead through strong architectural patterns
Collaborate with engineering teams to maintain consistency across the platform