Principal Platform Engineer (People Systems)

Quick Facts

• Role: Principal Platform Engineer (People Systems)

• Focus: Configuration-first platform architecture for recruiting, onboarding, core HR, payroll, compensation, and benefits

Description

You will architect and evolve a People Systems platform where features are defined through obsessively versioned configuration that is interpreted at runtime. The role requires making core architectural decisions that deliver scalable, secure, and resilient systems—enabling teams to change system behavior without waiting for releases. You will collaborate closely with product managers and cross-functional engineering teams to understand problems deeply and translate business needs into platform strategy.

Responsibilities

• Architect applications configuration-first (separate versioned configuration vs code; keep both in one coherent model; surface platform gaps as concrete requirements)

• Work fluently across heterogeneous components, choosing patterns and guarantees that fit each environment (correctness-critical vs high-scale, latency-sensitive search/UI/AI services)

• Set core platform patterns: canonical document model, saga-based orchestration, and a clear line between recorded facts and decisions

• Guarantee data integrity, consistency, and privacy with enforcement at read/write boundaries so unsafe access is inexpressible

• Own end-to-end system design for new initiatives, starting with systems impact analysis across the platform ecosystem

• Design APIs with strong contracts and clear versioning so many consuming teams can build confidently

• Lead design and coding of modular, domain-driven services on Java/Kotlin with Spring Boot and cloud-native infrastructure; set testing strategy and maintain the quality bar in code review

• Bring an AI-native SDLC and agent orchestration (specify/compose/evaluate/sandbox/promote agents; deterministic fallbacks; tool interfaces over platform APIs; structured-output validation)

• Define reliability: SLOs, observability, incident response, and zero-downtime deployments; diagnose complex distributed-systems failures; tune high-volume batch and real-time pipelines

• Establish architecture standards, reference designs, and blueprints; serve as go-to technical authority for partner teams

• Drive decisions balancing innovation, scalability, reliability, and compliance; present technical strategy and modernization options to leadership

• Mentor and elevate engineers across teams to grow organizational technical capacity

• Automate database management and performance tuning; integrate AI/ML for intelligent configuration recommendations, workload-aware scheduling, and LLM-powered operator workflows

• Design and maintain secure, versioned microservices exposing database lifecycle operations as APIs with correctness under concurrency and graceful handling of partial failures

• Integrate database automation into CI/CD with automated testing, canary rollouts, and rollback capabilities

• Develop monitoring/alerting and health-check systems for database health; build self-healing mechanisms to detect and remediate known failure patterns

Requirements

• 15+ years of professional software engineering in system design, technical architecture, or solution architecture for large-scale enterprise solutions

• BS/MS in Computer Science, Software Engineering, or related field (or equivalent practical experience)

• Led architecture of a configuration-driven platform for multiple teams (metadata models, multi-tenancy, extensibility, versioned APIs, safe publish/promote/rollback of configuration)

• Proficient across technology stacks; built components of different character within one system (interpreters/compilers, query/authorization, workflow/state engines, search/reporting, UI runtimes)

• Distributed-systems depth (consistency models, fault tolerance, partitioning, failure-mode analysis) with high availability and horizontal scale

• Strong event-driven and event-sourced architecture (saga/workflow orchestration, streaming platforms, change data capture, transactional data stores)

• Experience evolving schemas and contracts at scale with backward-compatible metadata/configuration changes and safe migration of in-flight data and workflows

• Deep proficiency in Spring Boot (or equivalent) and microservices patterns; async fluency (virtual threads Java 21+, coroutines, or reactive programming)

• Expert use of relational or NoSQL databases at scale (schema design, query optimization, migrations, transaction management)

• Hands-on AI-native SDLC and agent orchestration (agents under tests/review; tool calling; graph orchestration; context management; structured-output validation; deterministic fallbacks)

• Cloud-native production experience on AWS (or comparable) including Kubernetes, IAM, cost governance, monitoring/alerting, incident response, and post-mortem culture

• Set technical direction across multiple teams without direct authority; translate business requirements into designs and communicate trade-offs

Preferred Qualifications

• Built expression languages, rule engines, or policy engines (typed ASTs, multiple execution backends) with differential/conformance testing

• Experience decomposing monolithic enterprise systems into interoperable service-based platforms

• Background in CQRS, event sourcing, or saga-based orchestration for long-running workflows

• Familiarity with compliance/audit requirements for sensitive personal data (PII, regulated HR data) and AI governance (drift, bias, explainability, audit trails)

• Production experience integrating LLMs/ML into backend services (inference APIs, RAG, vector search, embeddings, evaluation, observability)

• Track record leading technical decisions cross-functionally and improving reliability, correctness, cost ownership, and evaluation-driven development

• Designed APIs consumed by many teams (versioning, contract stability, documentation) and integrated via APIs/ETL/event streams

• Strong developer experience for platform consumers (SDKs, local test harnesses, simulation environments, self-service onboarding)

• Experience with AI-assisted development tools and AI-augmented operations (predictive alerting, automated RCA, self-healing runbooks)

• Evaluation, guardrail, and human-in-the-loop strategies for consequential AI systems

• Hands-on chaos engineering and advanced reliability practice (failure injection, game days, capacity modeling, traffic shaping)